基于模糊逻辑的移动机器人智能控制器设计(英文)
模糊控制的移动机器人的外文翻译.doc
1998年的IEEE国际会议上机器人及自动化Leuven ，比利时1998年5月一种实用的办法--带拖车移动机器人的反馈控制F. Lamiraux and J.P. Laumond拉斯，法国国家科学研究中心法国图卢兹{florent ,jpl}@laas.fr摘要本文提出了一种有效的方法来控制带拖车移动机器人。
轨迹跟踪和路径跟踪这两个问题已经得到解决。
接下来的问题是解决迭代轨迹跟踪。
并且把扰动考虑到路径跟踪内。
移动机器人Hilare的实验结果说明了我们方法的有效性。
1引言过去的8年，人们对非完整系统的运动控制做了大量的工作。
布洛基[2]提出了关于这种系统的一项具有挑战性的任务，配置的稳定性，证明它不能由一个简单的连续状态反馈。
作为替代办法随时间变化的反馈[10,4,11,13,14,15,18]或间断反馈[3]也随之被提出。
从[5] 移动机器人的运动控制的一项调查可以看到。
另一方面，非完整系统的轨迹跟踪不符合布洛基的条件，从而使其这一个任务更为轻松。
许多著作也已经给出了移动机器人的特殊情况的这一问题[6,7,8,12,16]。
所有这些控制律都是工作在相同的假设下：系统的演变是完全已知和没有扰动使得系统偏离其轨迹。
很少有文章在处理移动机器人的控制时考虑到扰动的运动学方程。
但是[1]提出了一种有关稳定汽车的配置，有效的矢量控制扰动领域，并且建立在迭代轨迹跟踪的基础上。
存在的障碍使得达到规定路径的任务变得更加困难，因此在执行任务的任何动作之前都需要有一个路径规划。
在本文中，我们在迭代轨迹跟踪的基础上提出了一个健全的方案，使得带拖车的机器人按照规定路径行走。
该轨迹计算由规划的议案所描述[17] ，从而避免已经提交了输入的障碍物。
在下面，我们将不会给出任何有关规划的发展，我们提及这个参考的细节。
而且，我们认为，在某一特定轨迹的执行屈服于扰动。
我们选择的这些扰动模型是非常简单，非常一般。
它存在一些共同点[1]。
英文 单片机模糊逻辑控制器对永磁直流电动机的设计和应用
Engineering Applications of Artificial Intelligence 18 (2005) 881–890Single-chip fuzzy logic controller design and an application on a permanent magnet dc motorSinan PravadaliogluI.M.Y.O., Control Sys. Department, Dokuz Eylu ¨l University, Menderes cad, Istasyon sok 5, Buca, 35170 Izmir, Turkey Received 27 March 2004; accepted 11 March 2005 Available online 23 May2005AbstractThis paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dcconverters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibilityof a high-performance non-linear fuzzy logic controller which can be implemented byusing a general purpose microcontroller without modified fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.2005 Elsevier Ltd. All rights reserved.Keywords: Dc motor drive; Fuzzy logic controller; Microcontroller; Application; Simulation1.IntroductionIn switch-mode power supplies, the transformation of dc voltage from one level to another level is dc–dc conversion and accomplished by using dc–dc converter circuits, which offers higher efficiency than linear regulators. They have great importance in many practical electronic systems, including home appliances, computers and communication equipment. They are also widely used in industry, especially in switch-mode dc power supplies and in dc motor drive applications. The dc- dc converter accepts an unregulated dc input voltage and produces a controlled dc output at desired voltage level. They can step-up, step-down and invert the input dc voltage and transfer energy from input to output in discrete packets. The one disadvantage of dc–dc converters is noise. At every period to charge in discrete packets, it creates noise or ripple. The noise can be minimized using specific control techniques and with convenient component selection. There are well-knowncontrol techniques including pulse-width modulation (PWM) where the switch frequencyis constant and the duty cycle varies with the load.PWM technique affords high efficiency over a wide load range. In addition, because the switching frequency is fixed, the noise spectrum is relatively narrow, allowing simple low-pass filter techniques to reduce the peak-to- peak voltage ripple. For this same reason, PWM is popularwith telecom power supply applications where noise interference is of concern .The most important requirement of a control system for the dc–dc converter is to maintain the output voltage constant irrespective of variations in the dc input voltage and the load current. However, load changes affect the output transiently and cause significant deviations from the steady-state level of dc output voltage, which must be controlled to equal adesired level by the control systems. The inherent switching of a dc–dc converter results in the circuit components being connected periodically changing configurations, each configuration being described by a set of separate equations. Transient analysis and control system design for a converter is therefore difficult since a number of equations must be solved in sequence. Although the state-space averaging is the most commonly used model to obtain linear transfer functions to solve this problem, it neglects significant parts of non-linear behavior of dc–dc converters. Development of non-linear controllers for dc–dc converters have gained considerable attention in recent years.A fuzzy logic model based controller is chosen as the non-linear controller for this study. Fuzzy logic control (FLC) has been an important research topic. Despite the lack of concrete theoretical basis many successful applications on FLC were reported and various applications for dc–dc converters and electrical drives have been published and can be found in the literature (So et al., 1996, 1995;Mattavelli et al., 1997; Brandsetter and Sedlak, 1996; Hyo et al., 2001; Gupta et al., 1997;Zakharov, 1996; V as, 1998, 1999). FLC has a wide-spread application on the non-linear and complex systems as well as linear systems due to its capability to control the systems that might not have a transfer function between input and output variables. Experi-ences show that fuzzy control can yield superior results to those obtained by conventional control algorithms.In the meantime, new fuzzy microcontroller chips are available on the market and are able to execute fuzzy rules very fast with their mask programmed algorithms that have some drawbacks such as restriction in implementing any desired algorithm. Digital signal processing (DSP) integrated circuits (IC) are capable of computing and processing the system variables very quickly with high precision. But most of the DSP circuits are expensive and do not contain peripherals such as analog to digital (A/D) and digital to analog (D/A) circuits for conversion and PWM generator on chip,and need to be added externally. A fuzzy controller application among the others on dc–dc converters used a TMS320-DSP and fuzzy controller with an evaluation module plus some external chips. They were an A/D converter for feedback signal evaluation, a D/A converter for converting the calculated quantity into a control output and a PWM chip to generate the appropriate duty cycle for the semiconductor switching elements (So et al., 1995; Brandsetter and Sedlak, 1996). An implementation of an FLC with 8-bit conventional microcontroller is presented in Gupta et al. (1997) for dc–dc converters and a modified centroid method for defuzzication process is used to reduce the processing time of 8-bit microcontroller. However, this moded defuzzification technique increases the settling time of the system. A detailed simulation and experimental study on the closed-loop control of dc motor drive with FLC is carried out in Zakharov (1996) and a PC computer with an evaluation board is employed for FLC. The transient response of proportional integral (PI)-type current and speed controllers are compared to that of the FLC.The aim of the study presented in this paper is to design and to implement a high-performance fuzzy tuned PI controller for controlling the rotor speed of permanent magnet dc motor (PMDCM). We also provide a wayof designing such a controller in a cost effective way by using a general purpose single-chip microcontroller. This design is implemented with-outmaking the assumptions for the modification of defuzzification process which was presented in Guptaet al. (1997). This leads to an improved performance of the transient and steadystate of the closed-loop System.The experimental results are also compared to the simulation result obtained from MATLAB/SIMU-LINK.2.Permanent magnet dc motor and class C chopperA PMDCM fed via class C chopper can be described by the state-space form in the continuous time as follows:where R a is armature resistance (Ohm), L a is armature inductance (Henry), K aψis back electromotive force and torque constant (V/rad/s or Nm/A), J is total moment of inertia (kgm2) and B v is viscous friction constant (Nm/rad/s). V a（t）represents the voltage applied to armature by a class C type of chopper given in Fig. 1.The average value of armature voltage is a function of t on, period of chopping and the level of dc input voltage as shown in Fig. 2.In analog control systems, the repetitive sawtooth waveform is compared with the control voltage to generate the PWM gate signals to the MOSFETs employed in the chopper. The duty cycle is equal to the ratio between control voltage (E c) and the peak of sawtooth. The control voltage signal is generally obtained amplifying the difference between the actual output voltage and its desired value. Simple controls can be carried out using analog IC, such as operational amplifier circuits but sophisticated control tasks usually involves the using of digital ICs, microcontrollers or DSPs to support high-performance, repetitive, numeri-cally intensive tasks. Building a closed-loop control system, the actual output voltage can be sensed by a tacho generator which produces an output voltage proportional to armature rotation. Development and application of FLC in electrical drives have drawn greater attention in recent years (Vas, 1998, 1999).Fig. 1. Dc motor and Class C chopper.Fig. 2. V oltage waveforms of Class C type of chopper.3. Fuzzy controllerConventional controllers are derived from control theory techniques based on mathematical models of the process. They are characterized with design procedures and usually have simple structures. They yield satisfying results and are widely used in industry. However, in a number of cases, such as those, when parameter variations take place, or when disturbances are present, or when there is no simple mathematical model, fuzzy logic based control systems have shown superior performance to those obtained by conventional control algorithms.Fuzzy control is a method based on fuzzylogic. L.A.Zadeh's pioneering work in 1965, and his seminal paper in 1973 on fuzzy algorithms introduced the idea of formulating the control algorithm by logical rules. On the basis of the ideas proposed in this paper, Mamdani developed the first fuzzy control model in 1981. This then led to the industrial applications of fuzzy control.Fuzzy control can be described simplyas "control with sentences rather than equations" (Jan Jantsen1998). It provides an algorithm to convert a linguistic control strategy—based on expertknowledge—into an automatic control strategy. The essential part of a fuzzy controller is a set of linguistic rules which is called rule base.1. If error is Negative and change in error is Negative then output is Negative Big.2. If error is Negative and change in error is Zero then output is Negative Medium.The fuzzy rules are in the familiar if–then format and the "if side" is called the antecedent and the "then side"is called the consequent. The antecedents and the consequents of these if–then rules are associated with fuzzy concepts (linguistic terms), and they are often called fuzzy conditional statements. A fuzzy control rule is a fuzzy conditional statement in which the antecedent is a condition and the consequent is a control action.The fuzzy controller should execute the rules and compute a control signal depending on the measured inputs or conditions. There is no design procedure in fuzzy control such as root-locus design, pole placement design, frequencyresponse design, or stability design because the rules are often non-linear and close to the real world. Non-linearityis handled byrules, member-ship functions and the inference process.Described fuzzy logic model based on non-linear controller is developed and tested on real-time feedback control for the rotor speed of PMDCM. The speed feedback and fuzzy control algorithm block diagram are given in Fig. 3. The tacho-generator measures the actual rotor speed supplying input to the on-chip A/D converter. At the beginning of every kth switching cycle, the reference rotor speed w ref is compared with the actual rotor speed w act. The error (e(k)) and change of error (ce(k)) values of rotor speed are the inputs of the fuzzy control algorithm, which are defined asThe microcontroller calculates these inputs right after conversion from on-chip A/D converter. The fuzzy control algorithm is divided into three modules:(1) fuzzification, (2) decision-making or inference, (3)defuzzification.Fig. 3. Block diagram of drive circuit.In the fuzzification module, the error and change of error signals are evaluated by fuzzy singletons and their numerical values are converted into seven linguistic variables or subsets: PB (Positive Big), PM (Positive Medium), PS (Positive Small), ZE (Zero), NB (Negative Big), NM (Negative Medium) and NS (Negative Small). The fuzzification module calculates the degree of membership of every linguistic variable for given real values of error and change of error. The triangular shapes as given in Fig. 4a are used for smooth operation on membership functions.The calculated values of fuzzy variables are used in he decision-making process. Decision-making is infer-ing from control rules and linguistic variable defidefini-tions. There are seven sets for the error and seven sets for the change of error, and thus total 49 rules taking place for the whole control surface which are given in compact form in Table 1. This rule table can reflect experiences of the human experts.For each error and change of error, there are two overlapping memberships; therefore, all linguistic vari-ables except two has zero membership. Each two overlapping memberships of error and change of error will create four combinations as inference results.The maximum of these four inference results will have two parts, namely, the weighting factor w i and the degree of change of duty cycle y i. The min fuzzy implication rule of Mamdani is used to obtain the weighting factorwhich gives the membership degree of every relation (Lee, 1990). The inferred output ui of each rule isFig. 4. (a) Membership functions for error and change of error.(b) Enlargement for error membership function at a point x.Here, y i represents the centroid of membership function defining the ith rule output variable and can be stored in a look-up table for quick acquisition.Membership functions for change of output of FLC,which is the dutycycle for this application, is shown in Fig. 5According to the membership functions of the input,the output variables and the rule table, respectively, in Figs. 4a, 5 and Table 1, the control surface representa-tion is shown in Fig. 6.In defuzzification module, a crisp value for output is performed. Although the defuzzification process has many methods, the weighted average method is employed for this application because the operation of this method is computationally quite simple and takes less time in the computation process of microcontroller (Bart Kosko,1991; Ross, 1995). The output of defuzzification module can be represented byThe inferred output results and the weighing factors from each of the four rules are used in the equation given above to obtain a crisp value for the change of duty cycle. It is obvious that this calculation is the most time-consuming part of FLC and has computational complexity. The microcontroller output is the PWM duty cycle and defined asThis crisp value is the fuzzy controller actual output at the kth sampling period and is obtained from the previous value of control d(k-1) that is updated by Δd(k).In case of wide range changes of the drive operation, the response of FLC will be non-linear. This means that they should compensate either big positive errors like start-up or small negative errors during step changes.The main difference between the classical PI controller and fuzzy PI controller can be defined with their gains where fuzzy type has variable gain.4.Simulation resultsComputer simulation has an important role in the evaluation of power electronics and closed-loop con-troller designs (Pires and Silva, 2002). Fig. 7 shows the block diagram used in the MATLAB/SIMULINK program to simulate the closed-loop system which was performed on a PMDCM fed via class C chopper, presented in Fig. 3, having the following parameters:R a=0.271Ω,L a=0.41mH, J=0.00074 kgm2, B v=0.0013Nm/rad/s, K aψ= 0.0527V/rad/s.The simulation is performed in the time domain and the sampling period of A/D converter is T =1ms, dc input voltage V =24 and reference speed w ref=188.5 rad/s. The load torque T L was defined as a linear function of rotor speed w r having the operating point (T L =0.26Nm, w r =188.5 rad/s). KE1 in the block diagram given in Fig. 7 is the gain of tacho-generator, W ref1 and W ref , which are the step functions to change the sign of change of error in first sampling. The block of signal generator supplies 2 kHz sawtooth waveform having the magnitude of one; hence, the control signal generated bythe FLC will be equal to the duty cycle of the gate signals applied to the power switches. During simulation process when the change of error is calculated, noise on output of the A/D converter has appeared because change of error in time of 1ms is a very small value, so we have a problem of least significant bit in the number. To eliminate this effect additional gain blocks are introduced before the A/D converter block. So it is necessary to divide after A/D converter output and that is why KE and KCE are placed in the configuration.Fig. 8 shows the variation of actual rotor speed and armature current in time, when the load torque is increased by the amount of 0.17Nm at 0.4 s. The actual rotor speed deviates from the reference speed and comes back to the reference after transients are damped out. The increase of armature current is the response to increase of load torque.5.Hardware and software designsThe hardware setup for the proposed fuzzy logic algorithm was implemented in assembly programming, using 8-bit RISC (Reduced Instruction Set Computing) core microcontroller AT90S8535. A schematic diagram of the FLC with one of the Insulated Gate Bipolar Transistor (IGBT) drive circuits of Class C chopper is shown in Fig. 9. The microcontroller has 8K bytes of programmable flash memory, 512 bytes of internal random access memory, 8 channel 10 bit ADC, 10 bit PWM output, 16 different interrupt sources, an analog comparator, 32 programmable I/O lines, a bi-directional serial interface and has the ability to execute assembler instructions in a single clock cycle. The processing speed is one million instructions per megahertz crystal (Atmel Corporation).The universe of discourse for error and change of error are extended from -1024 to +1024 and the grades of each membership function (0–1) are also extended from 0 to 1024. In order to classify the fuzzy controller inputs, e(k) and ce(k), into seven fuzzy sets and to determine their memberships, 10 bit mathematical routines are used. Therefore, processing the on-chip 10 bit A/D converter sampling values and setting up the on-chip 10 bit PWM output for semiconductor switches are directlyachieved. As a result, the total system resolution is extended to 1/1024 insteadof 1/256 and better performance is obtained.The symmetrical and 50% overlapped triangular membership functions of e(k) and ce(k) simplify the calculations and its negative side is a reflection of the positive. The fuzzy subset linguistic rule table given in Table 1 is changed to integer numbers in order to suit assembly programming. A simple but effective algo-rithm is designed for the appropriate numerical values ofconverted linguistic rules. This is realized with the following equation according to calculated error values.The most important difference between the present paper and the papers cited in the references, is the developed algorithm for fast calculation.For error membership function,where e is the error value, X min=-1024 is the minimum value of the control variable as shown in Fig. 4a, s is the section number, starting from 0 to 6 for representing seven fuzzy subsets of errormembership and T g the width of every triangular membership function, here it is 256.For instance, let w ref =1000 rpm and w act =650 rpm. The calculation of error according to Eq. (2) is e(k)=350 rpm at a point x shown in Fig. 4b and from Eq. (8),is calculated. This value should be classified into fifth fuzzy set. It is well known that every e(k) belongs to at most two fuzzy sets. Therefore, the above error value has two overlapping fuzzy sets, fifth and sixth which are prevailed to linguistic PS and PM as shown in Fig. 4b. The fraction part of s always belongs to the membership function which has a positive slope. Therefore, for this example, the membership grade at point b shown in Fig.4b isμpm(e)=0.36719.The sum of membership grades of two symmetrical and 50% overlapped triangular fuzzy sets is always equal to 1.0μpm(e)+μps(e)=1.0.Hence the membership grade at point a isμps(e)=1.0-0.36719=0.63281.At the kth sampling time, change of error membership function is also evaluated using the same Eq. (8). The calculated values of fuzzy variables are used in the decision-making process. The flow chart of implemented assembler program is shown in Fig. 10.Membership functions of the output duty cycle for application on a PMDCM drive is presented in Fig. 5.A table is created for defuzzification process and stored as a look-up table containing the mean values of these membership functions of output and are used according to the weighted average method for defuzzification. The weighted average method for the defuzzification has an advantage over the other techniques in the case of limited memory structure of RISC core microcontrol-lers. A crisp value for the change of duty cycle is calculated from Eq. (6). According to Eq. (7) and the change of output of FLC shown in Fig. 5, the calculated change of the duty cycle is used to determine the new duty cycle for the application,The calculated value is used to update the PWM output by an interrupt routine shown in Fig. 10 at every 1ms (milli-second). Tests have shown that realization of the mentioned fuzzy interrupt routine lasts 450 ms (micro-second) processing time with 4MHz clock frequency. In this implementation, a D/A converter,which might be needed for output variable is eliminated by directly controlling the on–off periods of semicon-ductor switches via the on-chip PWM generator. When the motor is started from standstill, the error and change of error are estimated as positive at first sampling of rotor speed since their initial value at standstill are taken to be zero. The trend of change of error from standstill to steady state of rotor speed is negative; therefore, by changing the sign of change of error from positive estimated at first sampling to negative reduces the settling time of the rotor speed.6.ResultsAnother identical machine was coupled to the motor via their shafts and operated as a generator for loading. Two different loading conditions were applied on the motor. In the first loading condition, the generator terminals were closed to the resistive load of 2Ωand, the rotor speed and armature current variations in time during this loading case were recorded. These results are given in Fig. 11a (Upper trace: 1V/div., Lower trace: 0.2V/div. and 100mV/A. Time base: 200ms/div.). In the second loading condition, the resistance of 2Ωwas connected to the generator terminals while the rotor is running at reference speed. After the transients were damped out, the resistor was disconnected. The varia-tions of actual rotor speed and generator output current during this case were recorded and are given in Fig. 11b (Upper trace: 1V/div., Lower trace: 0.2V/div. And 100mV/A, Time base: 500ms/div.). It can be observed from the waveforms that the fuzzy logic controller responds to the step change on the load properly and brings the actual rotor speed back to the reference speed.7.ConclusionFuzzy logic controller is implemented without mod-ified methods byusing a general purpose low-cost microcontroller for the speed control of a PMDCM. All the tasks are carried out bya single chip reducing the cost of the system and program code optimization is achieved with developed effective algorithm. In our approach, the software-based decision table is used and only simple computations required in the on-line control of an FLC; therefore, higher sampling rate can be realized more easily when comparing with other type of control schemes. The developed fuzzy logic controller was simulated in MA TLAB/SIMULINK. Simulation and experimental results are compared in order to show the response of the FLC under loading conditions. It can be found that the experimental results are very close to the simulation results. The rise and settling times are reasonably smaller and there is no significant overshoot on the experimental results. The effect of saturation is not included into the motor and generator models; therefore, the simulation results deviate from the experimental results within a small percentage during loading transients. The experiments indicate that the implemented fuzzy controller has a high performance for real time control over a wide range of operating conditions.ReferencesAtmel Corporation. . A VR RISC datasheets, application notes, tools.Bart Kosko, 1991. Neural Networks and FuzzySystems. Prentice-Hall, New York. Brandsetter, P., Sedlak, P., 1996. Fuzzycontrol of electric drive using DSP, PEMC’96, Budapest, Hungary, pp. 3/462–3/466.Gupta, T., Boudreaux, R.R., Nelms, R.M., Hung, J.Y., 1997. Implementation of a fuzzycontroller for DC–DC converter using an inexpensive 8-b Microcontroller. IEEE Transactions on Industrial Electronics 44 (5).Hyo, S.Park, Hee, J.Kim., 2001. Simultaneous control of buck and boost DC–DC converter byfuzzycontroller. ISIE 2001 Proceed-ings, Pusan, Korea, pp. 1021–1025.Jan Jantsen, 1998. Design of FuzzyControllers, Technical Uni-versityof Denmark, Department of Automation, Pub. No: 98-E-864.Lee, C.C., 1990. Fuzzylogic in control systems: fuzzylogic controller.Part I, II. IEEE Transactions on Systems Man and Cybernetics 20(2), 404–435.Mattavelli, P., Rossetto, L., Spiazzi, G., Tenti, P., 1997. General purpose fuzzycontroller for DC–DC converters. IEEE Transac-tions on Power Electronics 12 (1).Pires, V.F., Silva, J.F.A., 2002. Teaching nonlinear modeling, simulation, and control of electronic power converters using.MA TLAB/SIMULINK. IEEE Transactions on Education 45 (3).Ross, T.J., 1995. FuzzyLogic with Engineering Applications.McGraw-Hill, New York.So, W.C., Tse, C.K., Lee, Y.S., 1995. An experimental fuzzy controller for DC–DC converters. IEEE Power Electronics Specialist Con-ference Record. pp. 1339–1345.So, W.C., Tse, C.K., Lee, Y.S., 1996. Development of a fuzzy logiccontroller for DC/DC converters: design, computer simulation and experimental evaluation. IEEE Transactions on Power Electronics11, 1.Vas, P., 1998. Sensorless Vector and Direct Torque Control. Oxford University Press, Oxford. Vas, P., 1999. Artificial Intelligence Based Electrical Machines and Drives. Oxford UniversityPress, New York.Zakharov, A., 1996. Investigation of dc servo drive with fuzzy logic control. M.Sc. Thesis, Technical Universityof Budapest, Depart-ment of Electrical Machines and Drives.。
可编程控制器外文翻译、中英文翻译、外文文献翻译
毕业设计中英文翻译院系专业班级姓名学号指导教师20**年 4 月Programmable Logic Controllers (PLC)1、MotivationProgrammable Logic Controllers (PLC), a computing device invented by Richard E. Morley in 1968, have been widely used in industry including manufacturing systems, transportation systems, chemical process facilities, and many others. At that time, the PLC replaced the hardwired logic with soft-wired logic or so-called relay ladder logic (RLL), a programming language visually resembling the hardwired logic, and reduced thereby the configuration time from 6 months down to 6 days [Moody and Morley, 1999].Although PC based control has started to come into place, PLC based control will remain the technique to which the majority of industrial applications will adhere due to its higher performance, lower price, and superior reliability in harsh environments. Moreover, according to a study on the PLC market of Frost and Sullivan [1995], an increase of the annual sales volume to 15 million PLCs per year with the hardware value of more than 8 billion US dollars has been predicted, though the prices of computing hardware is steadily dropping. The inventor of the PLC, Richard E Morley, fairly considers the PLC market as a 5-billion industry at the present time.Though PLCs are widely used in industrial practice, the programming of PLC based control systems is still very much relying on trial-and-error. Alike software engineering, PLC software design is facing the software dilemma or crisis in a similar way. Morley himself emphasized this aspect most forcefully by indicating [Moody and Morley, 1999, p. 110]:`If houses were built like software projects, a single woodpecker could destroy civilization.”Particularly, practical problems in PLC programming are to eliminate software bugs and to reduce the maintenance costs of old ladder logic programs. Though the hardware costs of PLCs are dropping continuously, reducing the scan time of the ladder logic is still an issue in industry so that low-cost PLCs can be used.In general, the productivity in generating PLC is far behind compared to other domains, for instance, VLSI design, where efficient computer aided design tools are in practice. Existent software engineering methodologies are not necessarily applicable to the PLC basedsoftware design because PLC-programming requires a simultaneous consideration of hardware and software. The software design becomes, thereby, more and more the major cost driver. In many industrial design projects, more than SO0/a of the manpower allocated for the control system design and installation is scheduled for testing and debugging PLC programs [Rockwell, 1999].In addition, current PLC based control systems are not properly designed to support the growing demand for flexibility and reconfigurability of manufacturing systems. A further problem, impelling the need for a systematic design methodology, is the increasing software complexity in large-scale projects.PLCs (programmable logic controllers) are the control hubs for a wide variety of automated systems and processes. They contain multiple inputs and outputs that use transistors and other circuitry to simulate switches and relays to control equipment. They are programmable via software interfaced via standard computer interfaces and proprietary languages and network options.Programmable logic controllers I/O channel specifications include total number of points, number of inputs and outputs, ability to expand, and maximum number of channels. Number of points is the sum of the inputs and the outputs. PLCs may be specified by any possible combination of these values. Expandable units may be stacked or linked together to increase total control capacity. Maximum number of channels refers to the maximum total number of input and output channels in an expanded system. PLC system specifications to consider include scan time, number of instructions, data memory, and program memory. Scan time is the time required by the PLC to check the states of its inputs and outputs. Instructions are standard operations (such as math functions) available to PLC software. Data memory is the capacity for data storage. Program memory is the capacity for control software.Available inputs for programmable logic controllers include DC, AC, analog, thermocouple, RTD, frequency or pulse, transistor, and interrupt inputs. Outputs for PLCs include DC, AC, relay, analog, frequency or pulse, transistor, and triac. Programming options for PLCs include front panel, hand held, and computer.Programmable logic controllers use a variety of software programming languages for control. These include IEC 61131-3, sequential function chart (SFC), function block diagram (FBD), ladder diagram (LD), structured text (ST), instruction list (IL), relay ladder logic (RLL), flow chart, C, and Basic. The IEC 61131-3 programming environment provides support for five languages specified by the global standard: Sequential Function Chart,Function Block Diagram, Ladder Diagram, Structured Text, and Instruction List. This allows for multi-vendor compatibility and multi-language programming. SFC is a graphical language that provides coordination of program sequences, supporting alternative sequence selections and parallel sequences. FBD uses a broad function library to build complex procedures in a graphical format. Standard math and logic functions may be coordinated with customizable communication and interface functions. LD is a graphic language for discrete control and interlocking logic. It is completely compatible with FBD for discrete function control. ST is a text language used for complex mathematical procedures and calculations less well suited to graphical languages. IL is a low-level language similar to assembly code. It is used in relatively simple logic instructions. Relay Ladder Logic (RLL), or ladder diagrams, is the primary programming language for programmable logic controllers (PLCs). Ladder logic programming is a graphical representation of the program designed to look like relay logic. Flow Chart is a graphical language that describes sequential operations in a controller sequence or application. It is used to build modular, reusable function libraries. C is a high level programming language suited to handle the most complex computation, sequential, and data logging tasks. It is typically developed and debugged on a PC. BASIC is a high level language used to handle mathematical, sequential, data capturing and interface functions.Programmable logic controllers can also be specified with a number of computer interface options, network specifications and features. PLC power options, mounting options and environmental operating conditions are all also important to consider.2、ResumeA PLC (programmable Logic Controller) is a device that was invented to replace the necessary sequential relay circuits for control.The PLC works by looking at its input and depending upon their state, turning on/off its outputs. The user enters a program, usually via software or programmer, which gives the desired results.PLC is used in many "real world" applications. If there is industry present, chance are good that there is a PLC present. If you are involved in machining, packing, material handling, automated assembly or countless other industries, you are probably already using them. If you are not, you are wasting money and time. Almost any application that needs some type of electrical control has a need for a PLC.For example, let's assume that when a switch turns on we want to turn a solenoid on for 5second and then turn it off regardless of how long the switch is on for. We can do this with a simple external timer. But what if the process included 10 switches and solenoids? We should need 10 external times. What if the process also needed to count how many times the switch individually turned on? We need a lot of external counters.As you can see the bigger the process the more of a need we have for a PLC. We can simply program the PLC to count its input and turn the solenoids on for the specified time.We will take a look at what is considered to be the "top 20" PLC instructions. It can be safely estimated that with a firm understanding of these instructions one can solve more than 80% of the applications in existence.Of course we will learn more than just these instruction to help you solve almost ALL potential PLC applications.The PLC mainly consists of a CPU, memory areas, and appropriate circuits to receive input/output data. We can actually consider the PLC to be a box full of hundreds or thousands of separate relay, counters, times and data storage locations,Do these counters,timers, etc. really exist? No,they don't "physically" exist but rather they simulated and be considered software counters, timers, etc. . These internal relays are simulated through bit locations in registers.What does each part do? Let me tell you.Input RelaysThese are connected to the outside world.They physically exsit and receive signals from switches,sensors,ect..Typically they are not relays but rather they are transistors.Internal Utility RelaysThese do not receive signals from the outside world nor do they physically exist.they are simulated relays and are what enables a PLC to eliminate external relays.There are also some special relays that are dedicated to performing only one task.Some are always on while some are always off.Some are on only once during power-on and are typically used for initializing data that was stored.CountersThese again do not physically exist. They are simulated counters and they can be programmed to count pulses.Typically these counters can count up,down or both up anddown.Since they are simulated,they are limited in their counting speed.Some manufacturers also include high-speed counters that are hardware based.We think of these as physically existing.Most times these counters can count up,down or up and down.TimersThese also do not physically exist.They come in many varieties and increments.The most common type is an on-delay type.Others include off-delays and both retentive and non-retentive types.Increments vary from 1ms through 1s.Output RelaysThere are connected to the outside world.They physically exist and send on/off signals to solenoids,lights,etc..They can be transistors,relays,or triacs depending upon the model chosen Data StorageTypically there are registers assigned to simply store data.They are usually used as temporary storage for math or data manipulation.They can also typically be used to store data when power is removed form the PLC.Upon power-up they will still have the same contents as before power was moved.Very convenient and necessary!A PLC works by continually scanning a program.We can think of this scan cycle as consisting of 3 important steps.There are typically more than 3 but we can focus on the important parts and not worry about the others,Typically the others are checking the system and updating the current internal counter and timer values,Step 1 is to check input status,First the PLC takes a look at each input to determine if it is on off.In other words,is the sensor connected to the first input on?How about the third...It records this data into its memory to be used during the next step.Step 2 is to execute program.Next the PLC executes your program one instruction at a time.Maybe your program said that if the first input was on then it should turn on the first output.Since it already knows which inputs are on/off from the previous step,it will be able to decide whether the first output should be turned on based on the state of the first input.It will store the execution results for use later during the next step.Step 3 is to update output status.Finally the PLC updates the status the outputs.It updates the outputs based on which inputs were on during the first step and the results executing your program during the second step.Based on the example in step 2 it would now turn on the firstoutput because the first input was on and your program said to turn on the first output when this condition is true.After the third step the PLC goes back to step one repeats the steps continuously.One scan time is defined as the time it takes to execute the 3 steps continuously.One scan time is defined as the time it takes to execute the 3 steps listed above.Thus a practical system is controlled to perform specified operations as desired.3、PLC StatusThe lack of keyboard, and other input-output devices is very noticeable on a PLC. On the front of the PLC there are normally limited status lights. Common lights indicate;power on - this will be on whenever the PLC has powerprogram running - this will often indicate if a program is running, or if no program is runningfault - this will indicate when the PLC has experienced a major hardware or software problemThese lights are normally used for debugging. Limited buttons will also be provided for PLC hardware. The most common will be a run/program switch that will be switched to program when maintenance is being conducted, and back to run when in production. This switch normally requires a key to keep unauthorized personnel from altering the PLC program or stopping execution. A PLC will almost never have an on-off switch or reset button on the front. This needs to be designed into the remainder of the system.The status of the PLC can be detected by ladder logic also. It is common for programs to check to see if they are being executed for the first time, as shown in Figure 1. The ’first scan’ input will be true on the very first time the ladder logic is scanned, but false on every other scan. In this case the address for ’first scan’ in a PLC-5 is ’S2:1/14’. With the logic in the example the first scan will seal on ’light’, until ’clear’ is turned on. So the light will turn on after the PLC has been turned on, but it will turn off and stay off after ’clear’ is turned on. The ’first scan’ bit is also referred to at the ’first pass’ bit.Figure 1 An program that checks for the first scan of the PLC4、Memory TypesThere are a few basic types of computer memory that are in use today.RAM (Random Access Memory) - this memory is fast, but it will lose its contents when power is lost, this is known as volatile memory. Every PLC uses this memory for the central CPU when running the PLC.ROM (Read Only Memory) - this memory is permanent and cannot be erased. It is often used for storing the operating system for the PLC.EPROM (Erasable Programmable Read Only Memory) - this is memory that can be programmed to behave like ROM, but it can be erased with ultraviolet light and reprogrammed.EEPROM (Electronically Erasable Programmable Read Only Memory) – This memory can store programs like ROM. It can be programmed and erased using a voltage, so it is becoming more popular than EPROMs.All PLCs use RAM for the CPU and ROM to store the basic operating system for the PLC. When the power is on the contents of the RAM will be kept, but the issue is what happens when power to the memory is lost. Originally PLC vendors used RAM with a battery so that the memory contents would not be lost if the power was lost. This method is still in use, but is losing favor. EPROMs have also been a popular choice for programming PLCs. The EPROM is programmed out of the PLC, and then placed in the PLC. When the PLC is turned on the ladder logic program on the EPROM is loaded into the PLC and run. This method can be very reliable, but the erasing and programming technique can be time consuming. EEPROM memories are a permanent part of the PLC, and programs can be stored in them like EPROM. Memory costs continue to drop, and newer types (such as flash memory) are becoming available, and these changes will continue to impact PLCs.5、Objective and Significance of the ThesisThe objective of this thesis is to develop a systematic software design methodology for PLC operated automation systems. The design methodology involves high-level description based on state transition models that treat automation control systems as discrete event systems, a stepwise design process, and set of design rules providing guidance and measurements to achieve a successful design. The tangible outcome of this research is to find a way to reduce the uncertainty in managing the control software development process, that is, reducing programming and debugging time and their variation, increasing flexibility of theautomation systems, and enabling software reusability through modularity. The goal is to overcome shortcomings of current programming strategies that are based on the experience of the individual software developer.A systematic approach to designing PLC software can overcome deficiencies in the traditional way of programming manufacturing control systems, and can have wide ramifications in several industrial applications. Automation control systems are modeled by formal languages or, equivalently, by state machines. Formal representations provide a high-level description of the behavior of the system to be controlled. State machines can be analytically evaluated as to whether or not they meet the desired goals. Secondly, a state machine description provides a structured representation to convey the logical requirements and constraints such as detailed safety rules. Thirdly, well-defined control systems design outcomes are conducive to automatic code generation- An ability to produce control software executable on commercial distinct logic controllers can reduce programming lead-time and labor cost. In particular, the thesis is relevant with respect to the following aspect Customer-Driven ManufacturingIn modern manufacturing, systems are characterized by product and process innovation, become customer-driven and thus have to respond quickly to changing system requirements.A major challenge is therefore to provide enabling technologies that can economically reconfigure automation control systems in response to changing needs and new opportunities. Design and operational knowledge can be reused in real-time, therefore, giving a significant competitive edge in industrial practice.Higher Degree of Design Automation and Software QualityStudies have shown that programming methodologies in automation systems have not been able to match rapid increase in use of computing resources. For instance, the programming of PLCs still relies on a conventional programming style with ladder logic diagrams. As a result, the delays and resources in programming are a major stumbling stone for the progress of manufacturing industry. Testing and debugging may consume over 50% of the manpower allocated for the PLC program design. Standards [IEC 60848, 1999; IEC-61131-3, 1993; IEC 61499, 1998; ISO 15745-1, 1999] have been formed to fix and disseminate state-of-the-art design methods, but they normally cannot participate in advancingthe knowledge of efficient program and system design.A systematic approach will increase the level of design automation through reusing existing software components, and will provide methods to make large-scale system design manageable. Likewise, it will improve software quality and reliability and will be relevant to systems high security standards, especially those having hazardous impact on the environment such as airport control, and public railroads.System ComplexityThe software industry is regarded as a performance destructor and complexity generator. Steadily shrinking hardware prices spoils the need for software performance in terms of code optimization and efficiency. The result is that massive and less efficient software code on one hand outpaces the gains in hardware performance on the other hand. Secondly, software proliferates into complexity of unmanageable dimensions; software redesign and maintenance-essential in modern automation systems-becomes nearly impossible. Particularly, PLC programs have evolved from a couple lines of code 25 years ago to thousands of lines of code with a similar number of 1/O points. Increased safety, for instance new policies on fire protection, and the flexibility of modern automation systems add complexity to the program design process. Consequently, the life-cycle cost of software is a permanently growing fraction of the total cost. 80-90% of these costs are going into software maintenance, debugging, adaptation and expansion to meet changing needs [Simmons et al., 1998].Design Theory DevelopmentToday, the primary focus of most design research is based on mechanical or electrical products. One of the by-products of this proposed research is to enhance our fundamental understanding of design theory and methodology by extending it to the field of engineering systems design. A system design theory for large-scale and complex system is not yet fully developed. Particularly, the question of how to simplify a complicated or complex design task has not been tackled in a scientific way. Furthermore, building a bridge between design theory and the latest epistemological outcomes of formal representations in computer sciences and operations research, such as discrete event system modeling, can advance future development in engineering design.Application in Logical Hardware DesignFrom a logical perspective, PLC software design is similar to the hardware design of integrated circuits. Modern VLSI designs are extremely complex with several million parts and a product development time of 3 years [Whitney, 1996]. The design process is normally separated into a component design and a system design stage. At component design stage, single functions are designed and verified. At system design stage, components are aggregated and the whole system behavior and functionality is tested through simulation. In general, a complete verification is impossible. Hence, a systematic approach as exemplified for the PLC program design may impact the logical hardware design.可编程控制器1、前言可编程序的逻辑控制器（PLC），是由Richard E.Morley 于1968年发明的，如今已经被广泛的应用于生产、运输、化学等工业中。
机器学习与人工智能领域中常用的英语词汇
机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。
智能控制相关英语词汇
智能控制英语词汇简介智能控制是一种利用人工智能技术来实现控制系统的优化、自适应和学习的方法。
智能控制涉及到许多英语词汇，本文将介绍其中的一些常见和重要的词汇，包括智能控制的定义、分类、应用、技术和方法等。
一、智能控制的定义智能控制（Intelligent Control）是一种综合运用人工智能（Artificial Intelligence）、机器学习（Machine Learning）、深度学习（Deep Learning）、神经网络（Neural Networks）、专家系统（Expert Systems）、知识库（Knowledge Base）、计算机视觉（Computer Vision）、自然语言处理（Natural Language Processing）等技术，来实现控制系统的优化、自适应和学习的方法。
智能控制器（Intelligence Controller）是一种能够根据环境变化和控制目标，自动调整控制策略和参数的控制器。
二、智能控制的分类智能控制可以根据所使用的技术和方法，分为以下几种类型：神经网络控制（Neural Network Control）：利用神经网络的非线性映射、自适应和学习能力，来实现复杂系统的建模和控制的方法。
模糊控制（Fuzzy Control）：利用模糊逻辑（Fuzzy Logic）来处理不确定性和模糊性，以及利用模糊推理（Fuzzy Inference）来模拟人类的思维和决策过程，来实现系统的控制的方法。
遗传算法控制（Genetic Algorithm Control）：利用遗传算法（Genetic Algorithm）来搜索最优的控制参数或策略，以实现系统的优化控制的方法。
专家系统控制（Expert System Control）：利用专家系统来存储和管理控制领域的知识，以及利用推理机（Inference Engine）来根据知识库（Knowledge Base）和事实库（Fact Base）进行推理，来实现系统的控制的方法。
智能控制技术英语
智能控制技术英语In the rapidly evolving world of technology,intelligent control systems have emerged as a crucial link between machines and humans. These systems, powered by advanced algorithms and sensors, enable precise andefficient control of various devices and processes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into control systems has further revolutionized the field, making it possible to adapt to changing environments and optimize performance in real-time. The core principle of intelligent control technologylies in its ability to process vast amounts of data, learn from past experiences, and make informed decisions. This data-driven approach allows the system to adapt to changesin the environment, predict future outcomes, and adjust its control strategies accordingly. The result is a more responsive, efficient, and reliable system that can handle complex tasks with minimal human intervention.One of the key applications of intelligent control technology is in robotics. Modern robots are equipped with sensors that provide them with a detailed understanding oftheir surroundings. By processing this information through AI and ML algorithms, robots can make split-second decisions about how to interact with their environment, ensuring smooth and efficient movement. This technology has revolutionized fields like manufacturing, where robots can now perform complex tasks with greater precision and speed than ever before.Another area where intelligent control technology has made significant impact is in the field of automation. By integrating AI and ML algorithms into control systems, itis now possible to automate processes that were previously considered too complex or unpredictable. This not only improves efficiency but also reduces the risk of human error, making operations safer and more reliable.However, the rise of intelligent control technology also presents new challenges. As systems become more autonomous, the need for robust safety measures and ethical guidelines becomes paramount. It is crucial to ensure that these systems are designed with safety as a top priority, and that they are capable of making ethical decisions in case of conflicting objectives.Despite these challenges, the future of intelligent control technology looks bright. With advances in AI, ML, and sensor technology, we can expect even moresophisticated control systems that are capable of handling even more complex tasks. As these systems become more pervasive in our daily lives, they will play a crucial role in shaping the future of industry, transportation, healthcare, and beyond.In conclusion, intelligent control technology has emerged as a key driver of innovation in modern society. By bridging the gap between machines and humans, it hasenabled unprecedented levels of precision, efficiency, and autonomy in various fields. While challenges remain, the potential of this technology is vast, and its impact on our future is sure to be profound.**智能控制技术：连接机器与人类的桥梁**在科技飞速发展的时代，智能控制系统已成为机器与人类之间的重要纽带。
移动机器人的那些避障方法你知多少？
移动机器人的那些避障方法你知多少？移动机器人是机器人的重要研究领域，人们很早就开始移动机器人的研究。
世界上第一台真正意义上的移动机器人是斯坦福研究院（SRI）的人工智能中心于1966年到1972年研制的，名叫Shakey，它装备了电视摄像机、三角测距仪、碰撞传感器、驱动电机以及编码器，并通过无线通讯系统由二台计算机控制，可以进行简单的自主导航。
Shakey的研制过程中还诞生了两种经典的导航算法：A*算法（the Asearch algorithm）和可视图法（the visibility graphmethod）。
虽然Shakey只能解决简单的感知、运动规划和控制问题，但它却是当时将AI应用于机器人的最为成功的研究平台，它证实了许多通常属于人工智能（AriTIficial Intelligence，AI）领域的严肃的科学结论。
从20世纪70年代末开始，随着计算机的应用和传感技术的发展，以及新的机器人导航算法的不断推出，移动机器人研究开始进入快车道。
移动机器人智能的一个重要标志就是自主导航，而实现机器人自主导航有个基本要求避障。
下面让我们来了解一下移动机器人的避障，避障是指移动机器人根据采集的障碍物的状态信息，在行走过程中通过传感器感知到妨碍其通行的静态和动态物体时，按照一定的方法进行有效地避障，最后达到目标点。
实现避障与导航的必要条件是环境感知，在未知或者是部分未知的环境下避障需要通过传感器获取周围环境信息，包括障碍物的尺寸、形状和位置等信息，因此传感器技术在移动机器人避障中起着十分重要的作用。
避障使用的传感器主要有超声传感器、视觉传感器、红外传感器、激光传感器等。
移动机器人避障常用的传感器1、激光传感器激光测距传感器利用激光来测量到被测物体的距离或者被测物体的位移等参数。
比较常用的测距方法是由脉冲激光器发出持续时间极短的脉冲激光，经过待测距离后射到被测目标，回波返回，由光电探测器接收。
根据主波信号和回波信号之间的间隔，即激光脉冲从激光器到被测目标之间的往返时间，就可以算出待测目标的距离。
移动机器人路径跟踪模糊控制系统设计及仿真
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０ 引言
利用模糊逻辑在移动机器人运动控制 中的优越性，采
点的位 置 ； 预瞄点确定以后 ，再根据移动机器人当前点相
具独特的优势 ， 本文结合 驾驶 员的丰富经验设计了移动机 器人的模 糊控制器 。设计 出移动机器人运 动控制进行仿真 的方法及程序流程 ， 然后对仿真结果进行了分析比较 ， 说
明模 糊控制器 的有效 性。
对于预瞄点的距离长短和方向偏差大小由角速度 ，线速度
模糊控制 器控制输 出移 动机器人 的线 速度 Ｖ和 角速度 ∞， 再 由移动机器人的运动模块完成运动控制。
１ 移动机器人模糊控制 系统设计
对于移动机器人的运动控制 问题 ，首先是要对经过机 器视觉检测和处理而成的规划路径进 行分析 ，其次就是根
踪运动 中的应用 。利用模糊逻辑在移动机器人运动控制中的优越性 ，结合驾驶员的丰富经验设计 了移动机器人 的模 糊控制器 ，包括一个预 瞄距 离确定器和一个 运动模糊控 制器 。设 计出移动机器人运 动控制进行仿真 的方 法及程 序流程 ，对其进行 计算机仿真验证控 制效 果， 对仿真 结果进行 了分析比较 ， 出模糊控制器达 到设 计要 得
基于模糊逻辑的机器人行为控制研究
基于模糊逻辑的机器人行为控制研究一、引言随着科技的飞速发展，机器人在各个领域的应用日益广泛，从工业生产到医疗服务，从太空探索到家庭生活。
机器人的行为控制成为了关键的研究课题，如何使机器人能够更加智能、灵活地适应复杂多变的环境和任务需求，是摆在研究者面前的重要挑战。
模糊逻辑作为一种处理不确定性和模糊性的有效工具，为机器人行为控制提供了新的思路和方法。
二、模糊逻辑的基本概念模糊逻辑是一种基于模糊集合理论的逻辑系统，与传统的布尔逻辑不同，它允许变量具有模糊的取值范围和隶属度。
在模糊逻辑中，一个概念不再是绝对的“是”或“否”，而是可以用一定程度的隶属度来表示。
例如，“速度快”这个概念可以用 0 到 1 之间的数值来表示不同的速度对“速度快”的隶属程度。
模糊逻辑的核心概念包括模糊集合、隶属函数和模糊推理。
模糊集合是对传统集合的扩展，它的元素具有不同的隶属度。
隶属函数用于定义元素对模糊集合的隶属程度，常见的隶属函数有三角形、梯形、高斯型等。
模糊推理则是根据已知的模糊规则和输入的模糊信息，得出模糊的输出结果。
三、机器人行为控制的需求与挑战机器人在实际应用中面临着各种各样的环境和任务，这些环境和任务往往具有不确定性和复杂性。
例如，在家庭服务机器人中，机器人需要根据不同的房间布局、家具摆放和人的行为来进行合理的移动和操作；在工业生产中，机器人需要适应不同的产品规格和生产流程的变化。
传统的机器人行为控制方法通常基于精确的数学模型和预设的规则，但这些方法在处理不确定性和模糊性时往往显得力不从心。
机器人可能会因为环境的微小变化或者传感器的误差而出现错误的行为，导致任务失败或者造成安全隐患。
因此，需要一种更加灵活和智能的控制方法，能够处理不确定性和模糊性，适应复杂多变的环境和任务。
四、模糊逻辑在机器人行为控制中的应用（一）传感器数据融合机器人通常配备了多种传感器，如视觉传感器、距离传感器、力传感器等。
这些传感器提供的信息往往存在噪声和不确定性。
基于模糊控制的自主移动机器人系统设计
２Ｏ Ｏ ６年 ｌ ２月
增刊
实
验
科
学
与 技
术
基 于模 糊 控 制 的 自主 移 动机 器 人 系统 设 计
粱莹林” ቤተ መጻሕፍቲ ባይዱ
（ 电子科技 大学
成都
６０５ ） １０４
摘要 ：针 对在 有 引导 线 的环 境 下 自动 导航 机 器人 寻迹 运 动 控 制 的 问题 ，介 绍 了一 种 基 于
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维普资讯
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机 器人 的关 键技 术是机 器人 的控制 。为 了控制
模糊控制器实现寻迹 自主移动机器人控制的解决方 案。实际应用证明了该方案的可行性。
２ 控制 系统 的总体设计
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Ａ Ｒ单片机的 自 导航机 器人运动控制 系统设计方案。在此基础上运 用模糊逻辑推理方法解决 Ｖ 动 了自 主式移动机器人 的导航 问题 。实际运行表明了该方法的有效性。 关键 词 ：模糊控 制 ；寻 线 ；Ａ Ｒ单 片机 Ｖ 中图分类 号 ：Ｔ ２ ２・ 文献标 识码 ：Ａ Ｐ４ ６ 文章编 号 ：１７ ４ ５ ２０ ）７－ １３— ４ ６２－ ５０（０６ ０ ００ ０