利用人工神经网络(ANN)预测变压器油中呋喃含量毕业论文外文文献翻译
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翻译日期： 2017.02.14
利用人工神经网络(ANN)预测变压器油中呋喃含量
阿联酋沙迦，美国沙迦大学，
摘要-在变压器中，变压器油中对油浸渍纸的老化状态评估的呋喃化合物浓度能有效的测量，呋喃含量浓度变化率对纤维素绝缘材料恶化率及其严重性的评估至关重要，这促进了变压器油中呋喃含量作为变压器状态评估及相应的资产管理的有效参数，在本文中，利用人工神经网络(ANN)对油参数和呋喃含量之间联系的研究，神经网络根据不同输入参数的组合已知纤维素纸降解相关的变压器来预测呋喃含量，这些输入参数是一氧化碳（CO）、二氧化碳(CO2)，水含量，酸度，击穿电压(BDV)。四十台变压器的真实数据结果显示，提议的模型能够预测呋喃含量，平均有90%的准确性。因此，这个模型提高了油化学试验分析和溶解气体分析（DGA）效率和评估变压器固体绝缘状况的能力。
I简介
作为电力系统网络反常的结果，公用事业已通过很好的发达资产管理计划争取优化他们的经营成本。接近发达的资产管理计划，有限条件评估和可靠的电力基础设施的剩余寿命的估计方法是主要的，电力变压器在电力系统网络是最重要的部分由于其高资金成本和直接影响网络可靠性。
运行中的电力变压器常遭受某些电的、热的、机械的、环境压力
以至于严重影响它的绝缘完整。这降低了变压器在运行中的能力和它的使用周期。因此，已开发的几种状态监测与诊断的方法实现准确的变压器状态评估。最重要和最可靠的监测技术是绝缘监测方法。这是因为变压器运行可靠性主要依靠它的绝缘系统去承受外部和内部压力的能力。此外，电力变压器剩余的使用期限等于它的纤维素纸绝缘的使用期限。因此，高效的变压器生存管理是通过接近可靠的诊断和它的纸绝缘状态评估来实现的。
浸过油的纤维素分解由于三个主要机制：水解，氧化降解（高温分解机制），热降解【1-3】。在变压器油中，通过加速老化试验和实地观察已经提出了若干参数来评估退化纤维素绝缘纸，如拉伸强度，聚合程度（DP）和呋喃含量。
变压器中呋喃含量浓度能有希望间接测量变压器纸绝缘老化。呋喃含量的测量包括测量变压器油中溶解的5个糠醛，其稳定性不同。糠醛有2-呋喃甲醛，5-羟甲基-2-呋喃甲醛，2—糠辑酸，呋喃甲醇，5-甲基-2-呋喃甲醛和2-乙酰基呋喃化合物组成。2-呋喃甲醛是最稳定的测量化合物；然而，除2-呋喃甲醛之外的所有的呋喃化合物检测绝缘油可达140℃
在实用程序，呋喃含量作为降解测量变压器固体绝缘中得到广泛接受，因为它是更实际的在线油采样，而不需要有一个变压器停电。变压器油中的呋喃含量的优势，油品质量测量和溶解气体分析，在变压器固体绝缘诊断方面。变压器油中糠醛含量和聚合度之间的直接关系已经发现在支持呋喃含量的有效性纤维素纸作为间接监测技术理想
实验中[2].评估电力变压器固体绝缘材料聚合度和糠醛之间的联系已被发现[3].糠醛的增加已经联系到拉伸强度的减少；然而，进一步的工作是需要正式这种关系[4]。
测量呋喃含量作为变压器状态评估和其生命周期的估计一种高效和实用工具已被调查。在一个巨大的矿物油浸式变压器群里中实施一项大规模调查，解释某些变压器油中呋喃含量浓度的临界值，表示变压器从正常到故障的工作状态[5]。余下的变压器寿命以变压器群体分布为基础使用统计方法来计算。另外，同意被提议的呋喃含量与纤维素纸水分含量有关[1].研究证明，在变压器油中总的呋喃浓度是对纸退化的一个更可靠的指示比每个特别的呋喃。尽管每个特别的呋喃化合物可以在溶解气体分析中说明不同类型的故障和严重程度[6]。一个全面的诊断方法是根据变压器内部气体和油的质量参数和糠醛，以及与关联糠醛与聚合程度之间的统计相关性[6]。趋势分析[7]利用DGA 的糠醛诊断变压器老化之间的相关性。
人工神经网络已经提出了糠醛作为突出的投入使用，以评估电力变压器老化。
电力变压器相对老化程度（RAD）计算使用纸和油的质量参数，如糠醛，一氧化碳（CO）浓度，界面张力和酸度作为输入[8].后来研究提出三个输入，它们是CO浓度，CO2浓度和糠醛，自它们是纸老化的产物【9】。然而，这些方法在利用中几乎不考虑成本效益，这是因为有呋喃含量作为输入模型相当的增加了模型成本，在本文中利用人工神经网络变压器油中呋喃含量的预测模型已经成熟，这个使用模型在
研究中利用油分解电压，含水量，酸度和CO,CO2的浓度作为输入模型。比较而言，在花费方面，获得这些参数不认为是一个大的负担；因此，在这研究中被提议的模型在实践中能有效的实施.
II材料和方法
A．变压器单元
在研究中使用的四十台变压器属于阿布扎比传输和迪斯派奇公司（特兰斯科）在模型中使用的培训和测试数据收集从变压器额定电压220/33千伏，132/11千伏，380/15千伏和33/11千伏及不同年龄的变压器的维修记录。在测试变压器所用的油是矿物环烷油。
B．油化学试验，溶解气体和呋喃含量
击穿电压是一个变压器油中杂质的测量，如氧化和水含量。根据IEC60156方法进行介电强度耐压试验。纤维素老化过程是在变压器油中的含水量增加的主要来源之一。在变压器油中，这介绍在油击穿电压和呋喃含量之间的关系。由于纸和油降解产生水。它减少了介电强度，绝缘材料加速老化过程中形成的高应力区域。因此，水，既促进又造成绝缘纸降解[1]化学实验室使用IEC60814方法测定含水量。变压器油中的酸性，有助于促进水解纤维素纸绝缘纸降解。这个过程最终产生葡萄糖变为变压器油呋喃的化合物。酸度是化学实验室根据ASTM D974衡量标准
CO和CO2浓度是通过进行溶解气体分析来测量的。超过一定阈值的一氧化碳和二氧化碳高浓度气体的存在是一个热降解纤维素纸的指示。因此，在实用工具中，CO2/CO比作为检测变压器纸绝缘故障的
变压器油中的呋喃含量利用人工神经网络预测通过呋喃含量与油参数的关联绝缘纸降解的作用。在本文中，三种不同方案研究依靠人工神经网络输入特征矢量。在第一个方案中输入产品是CO和CO2。油质量测试，第二种方案是以击穿电压，含水量和酸度为输入产品，第三种方案输入产品是第一二种的组合，因此第三种方案输入是CO，CO2，击穿电压，含水量和酸度。
概率论毕业论文外文翻译
Statistical hypothesis testing Adriana Albu，Loredana Ungureanu Politehnica University Timisoara,adrianaa@aut.utt.ro Politehnica University Timisoara,loredanau@aut.utt.ro Abstract In this article,we present a Bayesian statistical hypothesis testing inspection, testing theory and the process Mentioned hypothesis testing in the real world and the importance of, and successful test of the Notes. Key words Bayesian hypothesis testing; Bayesian inference;Test of significance Introduction A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by Ronald Fisher: "Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first."[1] Hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis. In frequency probability,these decisions are almost always made using null-hypothesis tests. These are tests that answer the question Assuming that the null hypothesis is true, what is the probability of observing a value for the test statistic that is at [] least as extreme as the value that was actually observed?) 2 More formally, they represent answers to the question, posed before undertaking an experiment,of what outcomes of the experiment would lead to rejection of the null hypothesis for a pre-specified probability of an incorrect rejection. One use of hypothesis testing is deciding whether experimental results contain enough information to cast doubt on conventional wisdom. Statistical hypothesis testing is a key technique of frequentist statistical inference. The Bayesian approach to hypothesis testing is to base rejection of the hypothesis on the posterior probability.[3][4]Other approaches to reaching a decision based on data are available via decision theory and optimal decisions. The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis. The critical region is usually denoted by the letter C. One-sample tests are appropriate when a sample is being compared to the population from a hypothesis. The population characteristics are known from theory or are calculated from the population.
人工神经网络原理及实际应用
人工神经网络原理及实际应用 摘要：本文就主要讲述一下神经网络的基本原理，特别是BP神经网络原理，以及它在实际工程中的应用。 关键词：神经网络、BP算法、鲁棒自适应控制、Smith－PID 本世纪初，科学家们就一直探究大脑构筑函数和思维运行机理。特别是近二十年来。对大脑有关的感觉器官的仿生做了不少工作，人脑含有数亿个神经元，并以特殊的复杂形式组成在一起，它能够在“计算＂某些问题（如难以用数学描述或非确定性问题等）时，比目前最快的计算机还要快许多倍。大脑的信号传导速度要比电子元件的信号传导要慢百万倍，然而，大脑的信息处理速度比电子元件的处理速度快许多倍，因此科学家推测大脑的信息处理方式和思维方式是非常复杂的，是一个复杂并行信息处理系统。1943年Macullocu和Pitts融合了生物物理学和数学提出了第一个神经元模型。从这以后，人工神经网络经历了发展，停滞，再发展的过程，时至今日发展正走向成熟，在广泛领域得到了令人鼓舞的应用成果。本文就主要讲述一下神经网络的原理，特别是BP神经网络原理，以及它在实际中的应用。 1.神经网络的基本原理 因为人工神经网络是模拟人和动物的神经网络的某种结构和功能的模拟，所以要了解神经网络的工作原理，所以我们首先要了解生物神经元。其结构如下图所示： 从上图可看出生物神经元它包括，细胞体：由细胞核、细胞质与细胞膜组成；
轴突：是从细胞体向外伸出的细长部分，也就是神经纤维。轴突是神经细胞的输出端，通过它向外传出神经冲动；树突：是细胞体向外伸出的许多较短的树枝状分支。它们是细胞的输入端，接受来自其它神经元的冲动；突触：神经元之间相互连接的地方，既是神经末梢与树突相接触的交界面。 对于从同一树突先后传入的神经冲动，以及同一时间从不同树突输入的神经冲动，神经细胞均可加以综合处理，处理的结果可使细胞膜电位升高；当膜电位升高到一阀值（约40mV），细胞进入兴奋状态，产生神经冲动，并由轴突输出神经冲动；当输入的冲动减小，综合处理的结果使膜电位下降，当下降到阀值时。细胞进入抑制状态，此时无神经冲动输出。“兴奋”和“抑制”，神经细胞必呈其一。 突触界面具有脉冲/电位信号转换功能，即类似于D/A转换功能。沿轴突和树突传递的是等幅、恒宽、编码的离散电脉冲信号。细胞中膜电位是连续的模拟量。 神经冲动信号的传导速度在1～150m/s之间，随纤维的粗细，髓鞘的有无而不同。 神经细胞的重要特点是具有学习功能并有遗忘和疲劳效应。总之，随着对生物神经元的深入研究，揭示出神经元不是简单的双稳逻辑元件而是微型生物信息处理机制和控制机。 而神经网络的基本原理也就是对生物神经元进行尽可能的模拟，当然，以目前的理论水平，制造水平，和应用水平，还与人脑神经网络的有着很大的差别，它只是对人脑神经网络有选择的，单一的，简化的构造和性能模拟，从而形成了不同功能的，多种类型的，不同层次的神经网络模型。 2.BP神经网络 目前，再这一基本原理上已发展了几十种神经网络，例如Hopficld模型，Feldmann等的连接型网络模型，Hinton等的玻尔茨曼机模型，以及Rumelhart 等的多层感知机模型和Kohonen的自组织网络模型等等。在这众多神经网络模型中，应用最广泛的是多层感知机神经网络。 这里我们重点的讲述一下BP神经网络。多层感知机神经网络的研究始于50年代，但一直进展不大。直到1985年，Rumelhart等人提出了误差反向传递学习算法（即BP算），实现了Minsky的多层网络设想，其网络模型如下图所示。它可以分为输入层，影层（也叫中间层），和输出层，其中中间层可以是一层，也可以多层，看实际情况而定。
毕业论文英文参考文献与译文
Inventory management Inventory Control On the so-called "inventory control", many people will interpret it as a "storage management", which is actually a big distortion. The traditional narrow view, mainly for warehouse inventory control of materials for inventory, data processing, storage, distribution, etc., through the implementation of anti-corrosion, temperature and humidity control means, to make the custody of the physical inventory to maintain optimum purposes. This is just a form of inventory control, or can be defined as the physical inventory control. How, then, from a broad perspective to understand inventory control? Inventory control should be related to the company's financial and operational objectives, in particular operating cash flow by optimizing the entire demand and supply chain management processes (DSCM), a reasonable set of ERP control strategy, and supported by appropriate information processing tools, tools to achieved in ensuring the timely delivery of the premise, as far as possible to reduce inventory levels, reducing inventory and obsolescence, the risk of devaluation. In this sense, the physical inventory control to achieve financial goals is just a means to control the entire inventory or just a necessary part; from the perspective of organizational functions, physical inventory control, warehouse management is mainly the responsibility of The broad inventory control is the demand and supply chain management, and the whole company's responsibility. Why until now many people's understanding of inventory control, limited physical inventory control? The following two reasons can not be ignored: First, our enterprises do not attach importance to inventory control. Especially those who benefit relatively good business, as long as there is money on the few people to consider the problem of inventory turnover. Inventory control is simply interpreted as warehouse management, unless the time to spend money, it may have been to see the inventory problem, and see the results are often very simple procurement to buy more, or did not do warehouse departments . Second, ERP misleading. Invoicing software is simple audacity to call it ERP, companies on their so-called ERP can reduce the number of inventory, inventory control, seems to rely on their small software can get. Even as SAP, BAAN ERP world, the field of
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吉林化工学院理学院 毕业论文外文翻译English Title（Times New Roman ，三号） 学生学号：08810219 学生姓名：袁庚文 专业班级：信息与计算科学0802 指导教师：赵瑛 职称副教授 起止日期：2012.2.27～2012.3.14 吉林化工学院 Jilin Institute of Chemical Technology
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java毕业论文外文文献翻译
Advantages of Managed Code Microsoft intermediate language shares with Java byte code the idea that it is a low-level language witha simple syntax , which can be very quickly translated intonative machine code. Having this well-defined universal syntax for code has significant advantages. Platform independence First, it means that the same file containing byte code instructions can be placed on any platform; atruntime the final stage of compilation can then be easily accomplished so that the code will run on thatparticular platform. In other words, by compiling to IL we obtain platform independence for .NET, inmuch the same way as compiling to Java byte code gives Java platform independence. Performance improvement IL is actually a bit more ambitious than Java bytecode. IL is always Just-In-Time compiled (known as JIT), whereas Java byte code was ofteninterpreted. One of the disadvantages of Java was that, on execution, the process of translating from Javabyte code to native executable resulted in a loss of performance. Instead of compiling the entire application in one go (which could lead to a slow start-up time), the JITcompiler simply compiles each portion of code as it is called (just-in-time). When code has been compiled.once, the resultant native executable is stored until the application exits, so that it does not need to berecompiled the next time that portion of code is run. Microsoft argues that this process is more efficientthan compiling the entire application code at the start, because of the likelihood that large portions of anyapplication code will not actually be executed in any given run. Using the JIT compiler, such code willnever be compiled.
基于BP神经网络的车型识别外文翻译
、外文资料 License Plate Recognition Based On Prior Knowledge Abstract - In this paper, a new algorithm based on improved BP (back propagation) neural network for Chinese vehicle license plate recognition (LPR) is described. The proposed approach provides a solution for the vehicle license plates (VLP) which were degraded severely. What it remarkably differs from the traditional methods is the application of prior knowledge of license plate to the procedure of location, segmentation and recognition. Color collocation is used to locate the license plate in the image. Dimensions of each character are constant, which is used to segment the character of VLPs. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing. The experimental results show that the improved algorithm is effective under the condition that the license plates were degraded severely. Index Terms - License plate recognition, prior knowledge, vehicle license plates, neural network. I. INTRODUCTION Vehicle License-Plate (VLP) recognition is a very interesting but difficult problem. It is important in a number of applications such as weight-and-speed-limit, red traffic infringement, road surveys and park security [1]. VLP recognition system consists of the plate location, the characters segmentation, and the characters recognition. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or under various lighting, weather condition and cleanliness of the plate. Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods [2], [3], [4], [5] reduce the complexity and increase the recognition rate by using some specific features of local VLPs and establishing some constrains on the position, distance from the camera to vehicles, and the inclined angles. In addition, neural network was used to increase the recognition rate [6], [7] but the traditional recognition methods seldom consider the prior knowledge of the local VLPs. In this paper, we proposed a new improved learning method of BP algorithm based on specific features of Chinese VLPs. The proposed algorithm overcomes the low speed convergence of BP neural network [8] and remarkable increases the recognition rate especially under the condition that the license plate images were degrade severely.
人工神经网络的发展及应用
人工神经网络的发展与应用 神经网络发展 启蒙时期 启蒙时期开始于1980年美国著名心理学家W．James关于人脑结构与功能的研究，结束于1969年Minsky和Pape~发表的《感知器》(Perceptron)一书。早在1943年，心理学家McCulloch和数学家Pitts合作提出了形式神经元的数学模型(即M—P模型)，该模型把神经细胞的动作描述为：1神经元的活动表现为兴奋或抑制的二值变化；2任何兴奋性突触有输入激励后，使神经元兴奋与神经元先前的动作状态无关；3任何抑制性突触有输入激励后，使神经元抑制；4突触的值不随时间改变；5突触从感知输入到传送出一个输出脉冲的延迟时问是0．5ms。可见，M—P模型是用逻辑的数学工具研究客观世界的事件在形式神经网络中的表述。现在来看M—P 模型尽管过于简单，而且其观点也并非完全正确，但是其理论有一定的贡献。因此，M—P模型被认为开创了神经科学理论研究的新时代。1949年，心理学家D．0．Hebb 提出了神经元之间突触联系强度可变的假设，并据此提出神经元的学习规则——Hebb规则，为神经网络的学习算法奠定了基础。1957年，计算机学家FrankRosenblatt提出了一种具有三层网络特性的神经网络结构，称为“感知器”(Perceptron)，它是由阈值性神经元组成，试图模拟动物和人脑的感知学习能力，Rosenblatt认为信息被包含在相互连接或联合之中，而不是反映在拓扑结构的表示法中；另外，对于如何存储影响认知和行为的信息问题，他认为，存储的信息在神经网络系统内开始形成新的连接或传递链路后，新 的刺激将会通过这些新建立的链路自动地激活适当的响应部分，而不是要求任何识别或坚定他们的过程。1962年Widrow提出了自适应线性元件(Ada—line)，它是连续取值的线性网络，主要用于自适应信号处理和自适应控制。 低潮期 人工智能的创始人之一Minkey和pape~经过数年研究，对以感知器为代表的网络系统的功能及其局限性从数学上做了深入的研究，于1969年出版了很有影响的《Perceptron)一书，该书提出了感知器不可能实现复杂的逻辑函数，这对当时的人工神经网络研究产生了极大的负面影响，从而使神经网络研究处于低潮时期。引起低潮的更重要的原因是：20世纪7O年代以来集成电路和微电子技术的迅猛发展，使传统的冯·诺伊曼型计算机进入发展的全盛时期，因此暂时掩盖了发展新型计算机和寻求新的神经网络的必要性和迫切性。但是在此时期，波士顿大学的S．Grossberg教授和赫尔辛基大学的Koho—nen教授，仍致力于神经网络的研究，分别提出了自适应共振理论(Adaptive Resonance Theory)和自组织特征映射模型(SOM)。以上开创性的研究成果和工作虽然未能引起当时人们的普遍重视，但其科学价值却不可磨灭，它们为神经网络的进一步发展奠定了基础。 复兴时期 20世纪80年代以来，由于以逻辑推理为基础的人工智能理论和冯·诺伊曼型计算机在处理诸如视觉、听觉、联想记忆等智能信息处理问题上受到挫折，促使人们
大学毕业论文---软件专业外文文献中英文翻译
软件专业毕业论文外文文献中英文翻译 Object landscapes and lifetimes Tech nically, OOP is just about abstract data typing, in herita nee, and polymorphism, but other issues can be at least as importa nt. The rema in der of this sect ion will cover these issues. One of the most importa nt factors is the way objects are created and destroyed. Where is the data for an object and how is the lifetime of the object con trolled? There are differe nt philosophies at work here. C++ takes the approach that con trol of efficie ncy is the most importa nt issue, so it gives the programmer a choice. For maximum run-time speed, the storage and lifetime can be determined while the program is being written, by placing the objects on the stack (these are sometimes called automatic or scoped variables) or in the static storage area. This places a priority on the speed of storage allocatio n and release, and con trol of these can be very valuable in some situati ons. However, you sacrifice flexibility because you must know the exact qua ntity, lifetime, and type of objects while you're writing the program. If you are trying to solve a more general problem such as computer-aided desig n, warehouse man ageme nt, or air-traffic con trol, this is too restrictive. The sec ond approach is to create objects dyn amically in a pool of memory called the heap. In this approach, you don't know un til run-time how many objects you n eed, what their lifetime is, or what their exact type is. Those are determined at the spur of the moment while the program is runnin g. If you n eed a new object, you simply make it on the heap at the point that you n eed it. Because the storage is man aged dyn amically, at run-time, the amount of time required to allocate storage on the heap is sig ni fica ntly Ion ger tha n the time to create storage on the stack. (Creat ing storage on the stack is ofte n a si ngle assembly in structio n to move the stack poin ter dow n, and ano ther to move it back up.) The dyn amic approach makes the gen erally logical assumpti on that objects tend to be complicated, so the extra overhead of finding storage and releas ing that storage will not have an importa nt impact on the creati on of an object .In additi on, the greater flexibility is esse ntial to solve the gen eral program ming problem. Java uses the sec ond approach, exclusive". Every time you want to create an object, you use the new keyword to build a dyn amic in sta nee of that object. There's ano ther issue, however, and that's the lifetime of an object. With Ian guages that allow objects to be created on the stack, the compiler determines how long the object lasts and can automatically destroy it. However, if you create it on the heap the compiler has no kno wledge of its lifetime. In a Ianguage like C++, you must determine programmatically when to destroy the
毕业论文5000字英文文献翻译
英文翻译 英语原文： . Introducing Classes The only remaining feature we need to understand before solving our bookstore problem is how to write a data structure to represent our transaction data. In C++ we define our own data structure by defining a class. The class mechanism is one of the most important features in C++. In fact, a primary focus of the design of C++ is to make it possible to define class types that behave as naturally as the built-in types themselves. The library types that we've seen already, such as istream and ostream, are all defined as classesthat is,they are not strictly speaking part of the language. Complete understanding of the class mechanism requires mastering a lot of information. Fortunately, it is possible to use a class that someone else has written without knowing how to define a class ourselves. In this section, we'll describe a simple class that we canuse in solving our bookstore problem. We'll implement this class in the subsequent chapters as we learn more about types,expressions, statements, and functionsall of which are used in defining classes. To use a class we need to know three things: What is its name? Where is it defined? What operations does it support? For our bookstore problem, we'll assume that the class is named Sales_item and that it is defined in a header named Sales_item.h. The Sales_item Class The purpose of the Sales_item class is to store an ISBN and keep track of the number of copies sold, the revenue, and average sales price for that book. How these data are stored or computed is not our concern. To use a class, we need not know anything about how it is implemented. Instead, what we need to know is what operations the class provides. As we've seen, when we use library facilities such as IO, we must include the associated headers. Similarly, for our own classes, we must make the definitions associated with the class available to the compiler. We do so in much the same way. Typically, we put the class definition into a file. Any program that wants to use our class must include that file. Conventionally, class types are stored in a file with a name that, like the name of a program source file, has two parts: a file name and a file suffix. Usually the file name is the same as the class defined in the header. The suffix usually is .h, but some programmers use .H, .hpp, or .hxx. Compilers usually aren't picky about header file names, but IDEs sometimes are. We'll assume that our class is defined in a file named Sales_item.h. Operations on Sales_item Objects
外文翻译---人工神经网络
英文文献 英文资料： Artificial neural networks (ANNs) to ArtificialNeuralNetworks, abbreviations also referred to as the neural network (NNs) or called connection model (ConnectionistModel), it is a kind of model animals neural network behavior characteristic, distributed parallel information processing algorithm mathematical model. This network rely on the complexity of the system, through the adjustment of mutual connection between nodes internal relations, so as to achieve the purpose of processing information. Artificial neural network has since learning and adaptive ability, can provide in advance of a batch of through mutual correspond of the input/output data, analyze master the law of potential between, according to the final rule, with a new input data to calculate, this study analyzed the output of the process is called the "training". Artificial neural network is made of a number of nonlinear interconnected processing unit, adaptive information processing system. It is in the modern neuroscience research results is proposed on the basis of, trying to simulate brain neural network processing, memory information way information processing. Artificial neural network has four basic characteristics: (1) the nonlinear relationship is the nature of the nonlinear common characteristics. The wisdom of the brain is a kind of non-linear phenomena. Artificial neurons in the activation or inhibit the two different state, this kind of behavior in mathematics performance for a nonlinear relationship. Has the threshold of neurons in the network formed by the has better properties, can improve the fault tolerance and storage capacity. (2) the limitations a neural network by DuoGe neurons widely usually connected to. A system of the overall behavior depends not only on the characteristics of single neurons, and may mainly by the unit the interaction between the, connected to the. Through a large number of connection between units simulation of the brain limitations. Associative memory is a typical example of limitations. (3) very qualitative artificial neural network is adaptive, self-organizing, learning ability. Neural network not only handling information can have all sorts of change, and in the treatment of the information at the same time, the nonlinear dynamic system itself is changing. Often by iterative process description of the power system evolution. (4) the convexity a system evolution direction, in certain conditions will depend on a particular state function. For example energy function, it is corresponding to the extreme value of the system stable state. The convexity refers to the function extreme value, it has DuoGe DuoGe system has a stable equilibrium state, this will cause the system to the diversity of evolution. Artificial neural network, the unit can mean different neurons process of the object, such as characteristics, letters, concept, or some meaningful abstract model. The type of network processing unit is divided into three categories: input unit, output unit and hidden units. Input unit accept outside the world of signal and data; Output unit of output system processing results; Hidden unit is in input and output unit, not between by external observation unit. The system The connections between neurons right value reflect the connection between the unit strength, information processing and embodied in the network said the processing unit in the connections. Artificial neural network is a kind of the procedures, and adaptability, brain style of information processing, its essence is through the network of transformation and dynamic behaviors have a