QUESTIONNAIRE LIST FOR NEW CEMENT PLANT-1
PVP、PKP 治疗胸腰椎骨质疏松性压缩骨折的疗效评价标准研究进展
PVP、PKP 治疗胸腰椎骨质疏松性压缩骨折的疗效评价标准研究进展丁惠宇;夏建龙【期刊名称】《山东医药》【年(卷),期】2014(000)014【总页数】3页(P101-103)【关键词】经皮椎体成形术;经皮椎体后凸成形术;椎体骨折;骨质疏松【作者】丁惠宇;夏建龙【作者单位】南京中医药大学第一临床医学院，南京210029;江苏省中医院【正文语种】中文【中图分类】R683.2经皮椎体成形术(PVP)和经皮椎体后凸成形术(PKP)是脊柱外科的新型微创技术。
经皮穿刺微创手术能迅速稳定骨折，缓解疼痛，缩短住院时间，促使患者早期下床活动，同时具有创伤小，手术时间短，操作简单微创，术后恢复快等特点，已成为治疗椎体压缩骨折的首选手段［1］。
PVP与PKP技术在数十年的发展过程中，产生了很多具有指导意义且贴近实际疗效的评价标准，本文现就各种评价标准进行综述，对比分析各自的特点，说明其优势及局限性，并提出对未来发展的一些展望，以进一步加强对临床应用的指导，增加手术成功率。
1 疼痛视觉模拟评分(VAS)该评分长期以来是衡量椎体成形术效果的主要指标，主要根据患者主观视觉感受，0～10分疼痛程度逐渐增加，直至剧烈疼痛、夜间无法入眠。
在PVP及PKP手术问世后相当长的时间里，绝大多数文献报告均以此作为衡量手术疗效的关键指标，并认为椎体成形微创手术能在术后4～48 h内显著缓解绝大多数椎体压缩性患者的疼痛，在1个月后疗效得到稳定［2］。
McGraw 等［3］通过对100 例、156 个椎体的骨质疏松性椎体压缩骨折患者进行治疗，发现97%的患者术后24h疼痛程度明显改善，99例平均随访21.5个月后，未发现有疼痛加重者。
学者们考虑其止痛机制是骨水泥的注入稳定了椎体骨折后出现的骨折微动，减轻了对痛觉神经末梢的刺激。
同时骨水泥凝固过程中能产生较多热量，使痛觉神经末梢处于高温下，影响了痛觉的传递。
Eck等［4］在一项Meta分析发现，施行PVP术者手术前后VAS分别为 8.36、2.68 分，而行 PKP 者分别为 8.06、3.46分，表明两种术式对疼痛的缓解均十分有效，而PVP效果较PKP更加显著。
RSQAS Questionnaire 中英文20101119
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Ro 5.2.1.1h Ro P 5.2.1.1i Ro 5.2.1.5. Ro 5.2.3.2. 5.3.1.
I I I I M I I M I
5.3.2. 5.3.3.
5.3.4. Ri 9.2.1.1. 11.1.1.2.
S H E Q C C C 1.1.1.2a 1.4.1.1. 2.4.1.2. 4.2.2.1. 4.2.3.1. 4.3.1.1o - Behaviour Based Safety (BBS) ?基于安全的行为准则 (BBS) ? Is a formal management meeting held at least once a year to review the SHEQ&Sec management systems ?是否至少 每年举行一次正式的管理会议以对SHEQ&保安体系进行评审？ Are waste disposal records retained as per legal requirements ?废物处理记录是否按照法规要求进行保留? Does the company maintain an up-to-date list of fully approved integrated subcontractors ?公司是否有一份适用的所 有经批准的完全分包商名单？ Does the company maintain an up-to-date list of fully approved non-integrated subcontractors ?公司是否有一份适用 的所有经批准的非完全分包商名单？ - adequate driver selection and training (including product segregation and compatibility, driver manual content, driver pre-start and post-loading checks, appropriate languages, defensive driver training, BBS, wearing seat-belts, parking of vehicles and segregation, weight limits in different countries)?仔细选择司机并给予充分培训（包括货物 隔离和配存规定，司机手册，司机开车前和装货后检查，适当的语言培训，预防性驾驶培训，BBS，安全 带，车辆的分隔和停放，不同国家的限载规定）？ - twist locks ?锁固装置 ? - cargo securing devices and materials ?货物紧固装置与材料 ? Is there evidence that overdue servicing or testing is monitored, documented and acted upon?是否有证据表明对所有 延迟的维护或测试服务进行跟踪、记录并采取了相应措施? Is there a documented defect reporting and rectification system in place for all equipment, incl. required follow-up ?是 否文件化的系统报告设备缺陷并记录纠正措施? Does the company have evidence that the purchase or lease of each vehicle/tank/tank container and associated equipment used by selected subcontractors is in line with the statutory requirements and industry standards ?是否能够 证明分包商采购或租用的车辆/槽罐/集装罐及配套设备也符合相关法规和行业标准 ? Does the company have evidence that selected subcontractors have a documented programme in place for preventive inspection and maintenance of their equipment ?是否能够证明分包商对其使用的设备进行系统的预防性检查和保 Does the company have evidence that selected subcontractors have a program in place that their vehicles/tanks/trailers/containers and their fittings are serviced on a regular basis ?是否能够证明分包商对其车辆/槽 罐/挂车/集装箱及其配件定期维护? Does the company have evidence that selected subcontractors are carrying out timely statutory inspections of tractor units and tanks/tank containers/trailers?是否能够证明分包商的拖车,槽罐/挂车已按相关法规进行检验? Is there a drivers manual distributed to all drivers in a language they can understand ?所有司机都有的其本国语言写 成的司机手册 Is housekeeping at a good standard (clean, tidy, paintwork, etc.) ?卫生状况良好（清洁、整齐、油漆划线）？ I I M I I
造价专业英语答案
project manager Management of construction field project cost and time control technical complexity construction methods design stage material substitute/alternatives regulatory agency material and equipment suppliers
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10. 材料和设备的运输时间 the delivery times of materials and project equipment
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Unit twelve
1. New Words and Expressions 2. Text Study 3. Exercises
1. 总承包商 2. 业主 3. 影响费用 4. 间接损失 5. 项目总进度计划 6. 项目网络图 7. 变更条款 8. 备忘录 9. 延迟提交索赔 10. 提出索赔
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Unit eight
1. New Words and Expressions 2. Text Study 3. Exercises
1. 投标明细或价值明细表 2. 污水处理厂 3. 地下管线 4. 道路改建 5. 开挖与回填 6. 筑堤 7. 延长英尺 8. 平方英尺 9. 延期罚款条款 10. 工程造价 11. 管理费和利润 12. 合同管理
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Ⅴ. Put the following Chinese into English
1. 2. 3. 4. 5. 6. 7. 8.
卷入诉讼-----公共工程-----承包商的注册-某此因素-----合同工期-----相对重要性---担保能力-----现有工作任务--
第24批高度关注物质(REACH)调查表最新样板2021年版
1Scope23please fill Yes or No 请填写是否使用The European Chemical Agency (ECHA) has published 2 new SVHCs on January 19, 2021. Currently there are total 211 Candidate List of substance of very high concern (SVHC) for Authorization. To ensure client product can meet REACH SVHC requirement, please survey and reply whether your product use the 2 new SVHCs and feedback to Sable before 2021/3/28.欧洲化学总署(ECHA)于2021年1月19日公告，新增2项物质于第24批高度关注物质清单(SVHC)，截至目前授权物质候选清单(SVHC)共有211项物质。
为确保客人所有产品符合REACH SVHC 的法规要求，请于期限内确认所有产品零件中是否有使用第24批高度关注物质清单(SVHC)，并于2021/3/28前回传至客人。
签署人保证已经获得公司充分授权，并保证本表格的内容以及所附测试报告完全准确、真实。
The below undersigned person declares that he/she is duly authorized by their organization to sign on its behalf and that the contents of this sheet and corresponding test reports are completely true and accurate.EU REACH 1097/2006/EC SVHC (REACH 24th SVHC)Does the item(s) being supplied contained SVHC above the threshold level 0.1% (weight by weight)?*if Yes, please fill detail information on below table *如果确认"使用"请填写下表详细信息第24批高度关注物质（REACH)调查表样板 request of survey about 24th SVHC供应商及其料件信息 Supplier and It's Part Information210143-24-8 20994-26-8 20822673-19-4 207693-98-1 2061072-63-5 205/ 20471850-09-4 20371868-10-5 202119313-12-1 201/ 200110-49-6 19998-54-4 198/197129-00-0;1718-52-1 4-tert-butylphenol 4-叔丁基苯酚Tris(4-nonylphenyl, branched and linear) phosphite (TNPP) with ≥0.1% w/w of 4-nonylphenol, branched and linear (4-NP)三 (4-壬基苯基，支鍊和直链)亚磷酸酯(TNPP)(4-壬基酚4-NP，支鍊和直链浓度≥ 0.1％重量比)Pyrene 芘Perfluorobutane sulfonic acid (PFBS) and its salts全氟丁烷磺酸及其盐类Diisohexyl phthalate二异己基邻苯二甲酸酯2-methyl-1-(4-methylthiophenyl)-2-morpholinopropan-1-one2-甲基-1-[4-(甲基硫代)苯基]-2-(4-吗啉基)-1-丙酮2-benzyl-2-dimethylamino-4'-morpholinobutyrophenone2-芐基-2-二甲基氨基-1-(4-吗啉苯基)丁酮2,3,3,3-tetrafluoro-2-(heptafluoropropoxy) propionic acid, itssalts and its acyl halides[covering any of their individual isomersand combinations thereof]2,3,3,3-四氟-2- (七氟丙氧基)丙酸，及其盐类与卤化酰类 (包括它们各自的异构物及其组合)2-methoxyethyl acetate 2-甲氧基乙酸乙酯bis(2-(2-methoxyethoxy)ethyl) ether双(2-(2-甲氧基乙氧基)乙基)醚（四乙二醇二甲醚）Butyl 4-hydroxybenzoate对羟基苯甲酸丁酯Dibutylbis(pentane-2,4-dionato-O,O')tin双(乙酰丙酮基)二丁基锡2-methylimidazole2-甲基咪唑1-vinylimidazole1-乙烯基咪唑。
PPAP提交要求清单Submission Requirement List
可销售产品的设计记录(图纸 ）Design records (drawings) of marketable products
5 ——对于专利部件/详细资料 For patented parts / detailed data
——对于所有其它部件/详细资料 For all other parts / detailed data
report, AOD result report - if any)
材料、性能试验结果(材质保证书/物性表）--是否使用二次再生料批复证据;周期检验试验计划 11 Material, Performance Test Result (Material Compliance Certificate / Material Property Table)- Evidence of reply on
using of recycling materials; cycle inspection test plan 12 初始过程研究（PPK） Initial Process Study (PPK) 13 测量系统分析研究 Research on measurement system analysis 14 具有资格的实验室文件 Documents of qualified labs
ppap提交要求清单ppapsubmissionrequirementlist要求requirements提交等级submissionlevel实际提交actualsubmission备注remarks等级1level等级2level等级3level3等级4level等级5levelppap提交要求清单ppapsubmissionrequirementlist零件提交保证书psw及附页如果适用partsubmissionwarrantypswattachedsheetsapplicable零件开发质量保证计划跟踪表初期流动管理表trackingformqualityassuranceplanpartdevelopmentiiinitialflowmanagementform工程更改文件如果有processchangedocumentsany可销售产品的设计记录图纸designrecordsdrawingsmarketableproducts对于专利部件详细资料patentedpartsdetaileddata对于所有其它部件详细资料allotherpartsdetaileddata顾客工程批准如果有要求customerprocessapprovalrequired设计fmeadfmea过程流程图processflowchart过程fmea含防错防混措施防止流出措施pfmeaincludingerrorpreventionmixingpreventionoutflowpreventionmeasures尺寸结果供应商全项目报告tsac全项目报告或tsac重点尺寸评价报告特采结果报告如有dimensionalresultssupplierfullitemreporttsacfullitemreporttsackeydimensionevaluationreportaodresultreportany材料性能试验结果材质保证书物性表是否使用二次再生料批复证据
Questionnaire for OTI FAR
Questionnaire for OTI FAR-EAST Technical Position CandidatePlease answer these questions. The answer should be based on your existing and true knowledge. Answer will be given to your answers, based which a final interview will be determined.请回答这些问题，答案应该是根据你现有的和真正的知识。
最终面试将取决于你对问题回答如何。
I. Technical Background1. Major courses1.1 What major courses you have learned in power system theory and analysis in undergraduate? What are your grades for them (please attached an official grade report)?主修课：电路理论、模拟电子/数字电子技术、电力电子技术、电机学、电机学、自动控制原理、电力系统分析、电力系统继电保护等。
成绩单请见附件。
1.2 What major courses you have learned in power system theory and analysis in graduate? What are your grades for them (please attached an official grade report)?本科1.3 What special studies you have done in power system theory and analysis? What are your grades them or study reports/papers you have written for them (please attach copies of your study report or papers if you have any)?大学学过的电力系统知识：1、电力系统的短路基本分类和计算方法。
Rx-360 Supplier Assessment Questionnaire Module 2
Site Quality Self-Assessmentbased onRx-360 Supplier Assessment Questionnaire Module 2, Site Specific InformationMerck KGaA, Darmstadt, Germany is an active member of the Rx 360 Consortium.As a trusted partner of our customers, we deliver quality -always.Relevant forSigma Aldrich Chemicals Pvt. Ltd. Plot #12, Bommasandra Jigani Link Road, Bangalore, Karnataka State, 560100 IndiaAn affiliate of Merck KGaA, Darmstadt, GermanyThe site self-assessment covers our quality management system for the following regulated applications: -Manufacturing of fine chemicalsInformationMerck KGaA, Darmstadt, Germany is an active member of the Rx 360 Consortium.This document is based on the Rx-360 Consortium’s Supplier Assessment Questionnaire template, Module 2. The contents of this questionnaire are built on the Rx-360 questionnaire version 2.0 intact with no question added or deleted.Rx-360's CEO/COO gave permission to Life Science to use the Rx-360 logo.Rx-360 Supplier Assessment Questionnaire :Site-Specific InformationPlease check here if additional documents are attached.SECTION 1. General Site Information1.1Site or Facility-Specific Name:Sigma Aldrich Chemicals Pvt. Ltd1.2Address:Plot #12, Bommasandra Jigani Link Road, Bangalore, Karnataka State, 560100 IndiaGPS Coordinates:Latitude: 12.8175756, Longitude: 77.679073600000041.3Phone:+91 80 6621 94001.4Email:*****************************1.5Fax:+91 80 6621 94501.6Website:SECTION 2. General Site Operating Information2.1What year did the site start operating? 19982.2What is the primary activity of the site? (e.g. manufacturing, distribution, etc.)Distribution, manufacturing, testing and packaging2.3To which, if any, subdivision of the parent company does the site belong?Life Sciences, an affiliate of Merck KGaA, Darmstadt, GermanyI (Supplier) confirm that the information provided in this questionnaire is correct and can be verified.Date:17.04.2023Title:Head of Quality。
毕业设计英文翻译
英文翻译系别机电工程系专业自动化班级 163803 学生姓名田琨学号 081470 指导教师张科、王敏Design Of Fuzzy Controllers1.1 IntroductionWhile it is relatively easy to design a PID controller, and the inclusion of the fuzzy rules creates a lot of extra design problems, and although a lot of introductory textbooks explain fuzzy controlthere are few general guidelines for setting the parameters of a simple fuzzy controller.The approach here is based on a three step design procedure,that builds on PID control:1. Start with a PID controller.2. Insert an equivalent, linear fuzzy controller.3. Make it gradually nonlinear.Guidelines related to the different components of the fuzzy controller will be introduced shortly. In the next three sections three simple realisations of fuzzy controllers are described: a table-based controller,an input-output mapping and a Takagi-Sugeno type controller. A short section summarises the main design choices in a simple fuzzy controller by introducing a check list. The terminology is based on an international standard which is underway (IEC, 1996).Fuzzy controllers are used to control consumer products, such as washing machines, video cameras, and rice cookers, as well as industrial processes, such as cement kilns, underground trains, and robots. Fuzzy control is a control method which is based on fuzzy logic. Just as fuzzy logic can be described simply as “computing with words rather than numbers”, fuzzy control can be described simply as “control with sentences rather than equations”.A fuzzy controller can include the empirical rules, and that is especially useful in operator controlled plants.Take for instance a typical fuzzy controller1. If error is Neg and change in error is Neg then output is NB2. If error is Neg and change in error is Zero then output is NM……The collection of rules is called a rule base. The rules are in the familiar if-then format, and formally the if-side is called the condition and the then-side is called the conclusion (more often, perhaps, the pair is called the antecedent-consequent orpremise-conclusion). The input value “Neg” is a linguistic short for the word Ne gative, the output value “NB” stands for Negative Big and “NM” for Negabive Meduim. The computer is able to execute the rules and compute a control signal depending on the measured inputs error and change in error.The objective here is to identify and explain the various design choices for engineers.In a rule based controller the control strategy is stored in a more or less natural language.The control strategy is isolated in a rule base opposed to an equation based description.A rule based controller is easy to understand and easy to maintain for a non-specialist end-user. An equivalent controller could be implemented using conventional techniques in fact,any rule based controller could be emulated in,say,Fortran-it is just that it is convenient to isolate the control strategy in a rule base for operator controlled systems.Fuzzy controllers are being used in various control schemes (IEC, 1996). The most obvious one is direct control, where the fuzzy controller is in the forward path in a feedback control system . The process output is compared with a reference, and if there is a deviation, the controller takes action according to the control strategy. In the figure, the arrows may be understood as hyper-arrows containing several signals at a time for multiloop control. The sub-components in the figure will be explained shortly. The controller is here a fuzzy controller, and it replaces a conventional controller, say, a PID (proportional-integral-derivative) controller.In feedforward control (Fig. 1) a measurable disturbance is being compensated. It requires a good model, but if a mathematical model is difficult or expensive to obtain，a fuzzy model may be useful. Figure 1 shows a controller and the fuzzy compensator, the process and the feedback loop are omitted for clarity. The scheme, disregarding the disturbance input,can be viewed as a collaboration of linear and nonlinear control actions; the controller C may be a linear PID controller, while the fuzzy controller F is a supplementary nonlinear controller.Fuzzy rules are also used to correct tuning parameters in parameter adaptive control schemes (Fig. 2). If a nonlinear plant changes operating point, it may be possible to change the parameters of the controller according to each operating point. This is called gain scheduling since it was originally used to change process gains. A gain scheduling controller contains a linear controller whose parameters are changed as a function of the operating point in a preprogrammed way .It requires thorough knowledge of the plant, but it is often a good way to compensate for nonlinearitiesand parameter variations. Sensor measurements are used as scheduling variables that govern the change of the controller parameters, often by means of a table look-up.Whether a fuzzy control design will be stable is a somewhat open question. Stablity concerns the system’s ability to converge or stay close to an equilibrium. A stable linear system will converge to the equilibrium asymptotically no matter where the system state variables start from. It is relatively straight forward to check for stability in linear systems,for example by checking that all eigenvalues are in the left half of the complex plane.For nonlinear systems, and checking that all eigenvalues are in the left half of the complex plane.For nonlinear systems, and fuzzy systems are most often nonlinear,the stability concept is more complex.A nonlinear system is said to be asymptotically stable if,when it starts close to an equilibrium,it will converge to it.Even if it just stays close to the equilibrium,without converging to it, it is said to be stable(in the sense of Lyapunov).To check conditions for stability is much more difficult with nonlinear systems,partly because the system behaviour is also influenced by the signal amplitudes apart from the frequencies.The literature is somewhat theoretical and interested readers are referred to Driankov,Hellendoorn & Reinfrank (1993)or Passino & Yurkovich(1998).They report on four methods ( Lyapunov functions, Popov, circle, and conicity),and they give several references to scientific papers.Figure1：Feedforward controlIt is characteristic, however, that the methods give rather conservative results, which translate into unrealistically small magnitudes of the gain factors in order to guarantee stability. Another possibility is to approximate the fuzzy controller with a linear controller, and then apply the conventional linear analysis and design procedures on the approximation. It seems likely that thestabilitymargins of the nonlinear system would be close in some sense to the stability margins of the linear approximation depending on how close the approximation is. This paper shows how to build such a linear approximation, but the theoretical background is stillunexplored.There are at least four main sources for finding control rules (Takagi & Sugeno in Lee, 1990).1. Expert experience and control engineering knowledge. One classical example is the operator’s handbook for a cement kiln (H olmblad & Ostergaard, 1982). The most common approach to establishing such a collection of rules of thumb, is to question experts or operators using a carefully organized questionnaire.2. Based on the operator’s control action rules can be deduced from ob servations of an operator’s control actions or a log book.This rules express input-output relationships.Figure 2：Fuzzyparameter adaptive control3. Based on a fuzzy model of the process. A linguistic rule base may be viewed as an inverse model of the controlled process. Thus the fuzzy control rules might be obtained by inverting a fuzzy model of the process. This method is restricted to relatively low order systems, but it provides an explicit solution assuming that fuzzy models of the open and closed loop systems are available (Braae&Rutherford in Lee, 1990). Another approach is fuzzy identification (Tong; Takagi & Sugeno; Sugeno – all in Lee, 1990; Pedrycz, 1993) or fuzzy model-based control (see later).4. Based on learning. The self-organising controller is an example of a controller that finds the rules itself. Neural networks is another possibility.There is no design procedure in fuzzy control such as root-locus design and the frequency response design, pole placement design, or stability margins, because the rules are often nonlinear. Therefore we will settle for describing the basic components and functions of fuzzy controllers, in order to recognise and understand the various options in commercial software packages for fuzzy controller design.There is much literature on fuzzy control and many commercial software tools (MIT, 1995), but there is no agreement on the terminology, which is confusing. Thereare efforts, however, to standardise the terminology, and the following makes use of a draft of a standard from the International Electrotechnical Committee (IEC, 1996). Throughout, letters denoting matrices are in bold upper case, for example A; vectors are in bold lower case,for example X; scalars are in italics, for example n; and operations are in bold, for example min.1.2 Structure of a fuzzy controllerThere are specific components characteristic of a fuzzy controller to support a design procedure.In the block diagram in Fig. 3, the controller is between a preprocessing block and a post-processing block. The following explains the diagram block by block.Figure 3: Blocks of a fuzzy controller.1.3 PreprocessingThe inputs are most often hard or crisp measurements from some measuring equipment, rather than linguistic. A preprocessor, the first block in Fig. 3, conditions the measurements before they enter the controller. Examples of preprocessing are:1. Quantisation in connection with sampling or rounding to integers;2. Normalisation or scaling onto a particular, standard range;3. Filtering in order to remove noise;4. Averaging to obtain long term or short term tendencies;5. A combination of several measurements to obtain key indicators; and6. Differentiation and integration or their discrete equivalences.A quantiser is necessary to convert the incoming values in order to find the best level in a discrete. Assume, for instance, that the variable error has the value 4.5, but the universe is u= (-5,-4,….,0….,4,5). The quantiser rounds to 5 to fit it to the nearest level. Quantisation is a means to reduce data, but if the quantisation is too coarse the controller may oscillate around the reference or even become unstable.Nonlinear scaling is an option . In the FL Smidth controller the operator is asked to enter three typical numbers for a small, medium and large measurement respectively (Holmblad & Østergaard, 1982). They become break-points on a curvethat scales the incoming measurements (circled in the figure). The overall effect can be interpreted as a distortion of the primary fuzzy sets. It can be confusing with both scaling and gain factors in a controller, and it makes tuning difficult. When the input to the controller is error, the control strategy is a static mapping between input and control signal. A dynamic controller would have additional inputs, for example derivatives, integrals, or previous values of measurements backwards in time. These are created in the preprocessor thus making the controller multi-dimensional, which requires many rules and makes it more difficult to design.The preprocessor then passes the data on to the controller.1.4 FuzzificationThe first block inside the controller is fuzzification, which converts each piece of input data to degrees of membership by a lookup in one or several membership functions. The fuzzification block thus matches the input data with the conditions of the rules to determine how well the condition of each rule matches that particular input instance. There is a degree of membership for each linguistic term that applies to that input variable.1.5 Rule BaseThe rules may use several variables both in the condition and the conclusion of the rules.The controllers can therefore be applied to both multi-input-multi-output (MIMO) problems and single-input-single-output (SISO) problems. The typical SISO problem is to regulate a control signal based on an error signal. The controller may actually need both the error, the change in error, and the accumulated error as inputs, but we will call it single-loop control, because in principle all three are formed from the error measurement. To simplify, this section assumes that the control objective is to regulate some process output around a prescribed set point or reference. The presentation is thus limited to single-loop control.Rule formats basically a linguistic controller contains rules in the if-them format, but they can be presented in different formats. In many systems, the rules are presented to the end-user in a format similar to the one below,1. If error is Neg and change in error is Neg then output is NB2. If error is Neg and change in error is Zero then output is NM3. If error is Neg and change in error is Pos then output is Zero4. If error is Zero and change in error is Neg then output is NM5. If error is Zero and change in error is Zero then output is Zero6. If error is Zero and change in error is Pos then output is PM7. If error is Pos and change in error is Neg then output is Zero8. If error is Pos and change in error is Zero then output is PM9. If error is Pos and change in error is Pos then output is PBThe names Zero, Pos, Neg are labels of fuzzy sets as well as NB, NM, PB and PM(negative big, negative medium, positive big, and positive medium respectively). The same set of rules could be presented in a rational format, a more compact representation.Error Change in error OutputNeg Pos ZeroNeg Zero NMNeg Neg NBZero Pos NMZero Zero ZeroZero Neg NMPos Pos PBPos Zero PMPos Neg ZeroThe top row is the heading, with the names of the variables. It is understood that the two leftmost columns are inputs, the rightmost is the output, and each row represents a rule.This format is perhaps better suited for an experienced user who wants to get an overview of the rule base quickly. The relational format is certainly suited for storing in a relational data base.模糊控制器的设计1.1概述虽然设计一个PID控制器相对来说很容易,但模糊规则却提出了和多额外的设计问题;虽然有许多入门的课本讲述了模糊控制,但关于如何对一个简单的模糊控制器设立参数还没有多少指导.本文所讲到的方法就是关于PID控制的设计步骤,它分三步:1. 先以一个PID控制器开始。