完整版)4_毕业设计(论文)外文文献翻译_

1、模糊控制理论摘自 维基百科 2011年11月20日概述模糊逻辑广泛适用于机械控制。这个词本身激发一个一定的怀疑，试探相当于“仓促的逻辑”或“虚假的逻辑”，但“模糊”不是指一个部分缺乏严格性的方法，而这样的事实，即逻辑涉及能处理的概念，不能 被表达为“对”或“否”，而是因为“部分真实”。虽然遗传算法和神经 网络可以执行一样模糊逻辑在很多情况下，模糊逻辑的优点是解决这个 问题的方法，能够被铸造方面接线员能了解，以便他们的经验，可用于设 计的控制器。这让它更容易完成机械化已成功由人执行。历史以及应用模糊逻辑首先被提出是有Lotfi在加州大学伯克利分校在1965年 的一篇论文。他阐述了他的观点在197

2、3年的一篇论文的概念，介绍了语 言变量”，在这篇文章中相当于一个变量定义为一个模糊集合。其他研 究打乱了，第二次工业应用中，水泥窑建在丹麦，即将到来的在线1975。模糊系统在很大程度上在美国被忽略了，因为他们更多关注的是人工智能，一个被过分吹嘘的领域，尤其是在 1980年中期年代，导致在诚 信缺失的商业领域。然而日本人对这个却没有偏见和忽略，模糊系统引发日立的 Seiji Yasunobu和 Soji Yasunobu Miyamoto 的兴趣。,他于 1985 年的模拟,证 明了模糊控制系统对仙台铁路的控制的优越性。他们的想法是被接受了并将模糊系统用来控制加速、制动、和停车，当线于1987年

3、开业。1987 年另一项促进模糊系统的兴趣。在一个国际会议在东京的模糊研究那一年，丫amakawa论证 使用模糊控制,通过一系列简单的专用模糊 逻辑芯片,在一个“倒立摆“实验。这是一个经典的控制问题，在这一过程中,车辆努力保持杆安装在顶部用铰链正直来回移动。这次展示给观察者家们留下了深刻的印象，以及后来的实验，他登上一 Yamakawa?酉杯包含水或甚至一只活老鼠的顶部的钟摆。该系统在 两种情况下,保持稳定。Yamakawa最终继续组织自己的fuzzy-systems 研究实验室帮助利用自己的专利在田地里的时候。展示之后，日本工程师开发出了大范围的模糊系统用于工业领域和 消费领域的应用。198

4、8年，日本建立了国际模糊工程实验室，建立合作安 排48公司进行模糊控制的研究。松下吸尘器使用微控制器运行模糊算法去控制传感器和调整吸尘力。日立洗衣机用模糊控制器 Load-Weight,Fabric-Mix和尘土传感器及 自动设定洗涤周期来最佳利用电能、水和洗涤剂。佳能研制出的一种上相机使用电荷耦合器件（CCD）测量中的图像清晰的六个区域其视野和使用提供的信息来决定是否这个影像在焦点上 （清晰）。它也可以追踪变化的速率在镜头运动的重点，以及它的速度以防止控制超调。相机的模糊控制系统采用12输入,6个输入了解解现行清晰所提供的数据和其他6个输入测量CCD镜头的变化率的运动。输出 的位置是镜头。模

5、糊控制系统应用13条规则,需要1.1千字节记忆信息。另外一个例子是，三菱工业空调设计采用25加热规则和25冷却规 则。温度传感器提供输入，输出一个控制逆变器，一个压缩机气阀，风扇 电机。和以前的设计相比，新设计的模糊控制器增加五次加热冷却速度， 降低能耗24%增加温度稳定性的一个因素两个，使用较少的传感器。日本人对模糊逻辑的人情是反映在很广泛的应用范围上 ，他们一直 在研究或实现：例如个性和笔迹识别光学模糊系统，机器人,声控机器人 直升飞机。模糊系统的相关研究工作也在美国和欧洲进行着。美国环境保护署分析了模糊控制节能电动机，美国国家航空和宇宙航行局研究了模糊控 制自动太空对接。仿真结果表明，模

6、糊控制系统可大大降低燃料消耗。 如波音公司、通用汽车、艾伦-布拉德利、克莱斯勒、伊顿，和漩涡了模 糊逻辑用于低功率冰箱、改善汽车变速箱。在1995年美泰克公司推出的一个“聪明” 基于模糊控制器洗碗机，“一站式感应模块”包括热敏 电阻器,用来温度测量；电导率传感器，用来测量离子洗涤剂水平存在于 洗；分散和浊度传感器用来检测透射光测量失禁的洗涤，以及一个磁致伸缩传感器来读取旋转速率。这个系统确定最优洗周期任何载荷，获得最佳的结果用最少的能源、洗涤剂、和水。研究和开发还继续模糊应用软件，作为反对固件设计，包括模糊专 家系统模糊逻辑与整合神经网络和所谓的自适应遗传软件系统 ，其最终 目的是建立“自主学

7、习”模糊控制系统。模糊集输入变量在一个模糊控制系统是集映射到一般由类似的隶属度函 数,称为“模糊集”。转换的过程中，一个干脆利落的输入值模糊值称为 “模糊化”。一个控制系统也有各种不同的类型开关或“开关” ，连同它的模拟 输入输入,而这样的开关输入当然总有一个真实的价值等于要么 1或0, 但该方案能对付他们，简单的模糊函数，要么发生一个值或另一个。赋予了“映射输入变量的隶属函数和进入真理价值，单片机然后做 出决定为采取何种行动基于一套“规则”，每一组的形式。在一个例子里，有两个输入变量是“刹车温度”和“速度”，定义为 模糊集值。输出变量，“制动压力”，也定义为一个模糊集，有价值观像 “静”、“

8、稍微增大”“略微下降”，等等。这条规则本身很莫名其妙，因为它看起来好像可以使用，会干扰到与模 糊，但要记住,这个决定是基于一套规则。所有的规则都调用申请，使用模糊隶属度函数和诚实得到输入值，确定结果的规则。这个结果将被映射成一个隶属函数和控制输出变量的 真值。这些结果相结合，给出了具体的（“脆”）的答案,实际的制动压力，一个 过程被称为解模糊化，结合了模糊操作规则 推理“描述”模糊专家系 统”。传统的控制系统是基于数学模型的控制系统 ，描述了使用一个或更 多微分方程确定系统回应其输入。这类系统通常被作为“ PID控制器” 他们是产品的数十年的发展建设和理论分析，是非常有效的。如果PID和其他传

9、统的控制系统是如此的先进，何必还要模糊控制 吗？它有一些优点。在许多情况下，数学模型的控制过程可能不存在，或 太“贵”的认识论的计算机处理能力和内存，与系统的基于经验规则可 能更有效。此外,模糊逻辑都适合低成本实现基于廉价的传感器、低分辨率模 拟数字转换器,或8位单片机芯片one-chip 4比特。这种系统可以很容 易地通过增加新的规则升级来提高性能或添加新功能。在许多情况下，模糊控制可以用来改善现有的传统控制器系统通过增加了额外的情报 电流控制方法。模糊控的细节模糊控制器是很简单的理念上。它们是由一个输入阶段，一个处理阶 段,一个输出阶段。地图传感器输入级或其他输入，比如开关等等，到合适的隶

10、属函数和真理的价值。每一个适当的加工阶段调用规则和产生的 结果对每个人来说，然后结合结果的规则。最后,将结果输出阶段相结合 的具体控制输出回他的价值。最常见的形状是三角形的隶属度函数，尽管梯形和贝尔曲线也使用 但其形状通常比数量更重要曲线及其位置。 从三人至七人通常是适当的 覆盖曲线所需要的范围的一个输入值，或“宇宙的话语“在模糊术语。作为讨论之前，加工阶段是基于规则的集合的形式逻辑 IF - THEN 报表,那里的部分叫做“之前”和后来的部分被称为“随之”。典型的模 糊控制系统具有几十个规则。这条规则的价值采用真理“温”的输入,真值的“冷”，产生的结果, 在模糊集的“加热器“输出，“高”的价

11、值。这个结果是用来与其他规 则的结果，最终产生脆复合输出。很明显，越是真理价值的“冷”，真值 越高，“高”，但这并不一定就意味着输出本身会被设置为“高”，因为这是唯一准则在许多。在某些情况下，隶属函数可以修正“篱笆”相当 于形容词。模糊限制语包括“关于“常见，“近”、“接近”、“大约”、“很”、 “稍微”、“太”、“非常”、“有点”。这些操作可能有明确的定义,虽然可 能有很大差别的定义不同的实现。“非常”，因为一个典型的例子，广场 隶属函数；因为会员价值总是小于1,这减少了隶属函数。“非常”立方体 价值观提供更大的缩小，而“有点“扩大功能以平方根的计算。在实践中，模糊规则集，通常有几个来路综合

12、利用模糊运算 ，如,或 者,不,虽然再次定义每每变化，在一个受欢迎的定义，只是利用最小重量的雏形，而或采用最大值。还有一个不经营者一个隶属函数减去从1到给“补充性”功能。有几种方法可以定义一个规则的结果，而是一种最常见的和最简单 的是“极大极小“推理法，给出了输出隶属函数的真值所产生的前提。规则可以解决并联在硬件或软件。顺序结果所有的规则，其中的几 个方法。在理论上有几十个，每个都有各种各样的优点和缺点。“质心”的方法很受欢迎，在“的质心”的结果提供了清新的价值。 另一个方法是“高度”方法，它以价值的主要因素。方法更利于统治质 心与输出最大的区域，而高程法显然更利于规则和最大的输出值。模糊控制

13、系统的设计是基于经验方法，基本上一个系统的方法试 误。大致过程如下：1. 文件系统的操作规范和输入与输出。2. 文档模糊集的输入。3. 文件规则集。4. 确定解模糊化方法确定。5. 运行测试套件验证通过制度，调整细节的要求。6. 完整的文件，发布给生产。逻辑解释模糊控制尽管有几个困难出现给一个严谨的逻辑解释 If - The n 规则。作为 一个例子,解释一个规则,因为如果（温度是“冷”），那么（加热器是“高”） 由第一阶表达式冷一高和假设是一个输入这样冷是假的。然后公式冷一高是适用于任何一个师，因此任何不正确的控制提供了一种给 r o很明显, 如果我们考虑系统的先例的规则类定义一个分区这样一

14、个自相矛盾的 现象不会出现。在任何情况下它有时是不考虑两个变量和在一条规则没有某种功能的依赖。严谨的逻辑正当化中给出的模糊控制Hajek的书，被描绘成一个模糊控制理论的基本Hajek逻辑。在2005 Gerla模糊控制逻辑方法，提出了一种基于以下的想法。f模糊函数表示的系统与模糊 控制相结合，即：给定输入，是模糊集合可能的输出。然后给出一个可能 的输出的,我们把为真理程度的表示。更多的是任何系统的If - Then规则可转化为一个模糊的程序，在这种情况下模糊函数模糊谓词的解释 很好在相关的最小模糊 Herbrand模型。以这样一种方式成为一个章模 糊控制的模糊逻辑编程。学习过程成为一个问题属于

15、归纳逻辑理论。Fuzzy ControlFrom Wikipedia 20 November 2011OverviewFuzzy logic is widely used in machine control. The term itself inspires a certain skepticism, sounding equivalent to the method, rather to the fact that the logic involved can deal with conceptsthat cannot be expressed as true or false but r

16、ather as partially true. Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases,fuzzy logic to the problem can be cast in terms that operators can understand, so that their experience can be used in the design of the controller. This makes it easier to

17、mechanize tasks that are already successfullyperformed by a 1965 paper. He elaborated on a 1973 paper that introduced the concept of linguistic variables, which in this article equates to a variable defined as a fuzzy set. Other research followed, with the first industrialapplication, a cement kiln

18、built in Denmark, coming on line in 1975.Fuzzy systems were largelyignored in the U.S. because they were associatedwith artificial intelligence,a field that periodically oversells itself, especially in the mid-1980s, resulting in a lack of credibility within the commercial domain.The Japanese did no

19、t fuzzy systems was sparked by Seiji Yasunobu and Soji Miyamoto of Hitachi, who in 1985 provided simulations that demonstrated the superiority of fuzzy control systems for the Sendai railway. Their ideas were adopted, and fuzzy systems were used to control accelerating, braking, and stopping when th

20、e line opened in 佃 87.Another event in 1987 fuzzy systems. During an international meeting of fuzzy researchersin Tokyo that year, Takeshi Yamakawa demonstrated the use of fuzzy control, through a set of simple dedicated fuzzy logic chips, in an inverted pendulum experiment. This is a classic contro

21、l problem, in which a vehicle tries to keep a pole mounted on its top by a , as well as later experiments by Yamakawa in which a live mouse to the top of the pendulum. The system maintained stability in both cases. Yamakawa eventually went on to organizefuzzy-systems research lab to the field.Follow

22、ing such demonstrations, Japanese engineers developed a wide range of fuzzy systems for both industrial and consumer applications. In 1988 Japan established the Laboratory for International Fuzzy Engineering (LIFE), a cooperative arrangement between 48companies to pursue fuzzy research.Matsushita va

23、cuum cleaners use micro controllers running fuzzy algorithms to interrogate dust sensors and adjust suction power accordingly. Hitachi washing machines use fuzzy controllers to load-weight, fabric-mix, and dirt sensors and automatically set the wash cycle for the best use of power, water, and deterg

24、ent.Canon developed an autofocusing camera that uses a charge-coupled device (CCD) to measure the clarity of the image in six regions of its field of view and use the information provided to determine if the image is in focus.It also tracks the rate of change of lens movement during focusing, and co

25、ntrols its speed to prevent overshoot.The cameras fuzzy control system uses 12 inputs: 6 to obtain the current clarity data provided by the CCD and 6 to measure the rate of change of lens movement. The output is the position of the lens. The fuzzy control system uses 13 rules and requires 1.1 kiloby

26、tes of memory.As another example of a practical system, an industrial air conditioner designed by Mitsubishi uses 25 inverter, a compressor valve, and a fan motor. Compared to the previous design, the fuzzy controller by 24%, increases temperature stability by a factor of two, and uses fewer sensors

27、.The enthusiasmof the Japanesefor fuzzy logic is reflected in the wide range of other applications they ; optical fuzzy systems;robots, voice-controlled robot fuzzy systems is also proceeding in the US and Europe. The US Environmental Protection Agency greatly reduce fuelconsumption. Firms such as B

28、oeing, General Motors, Allen-Bradley, Chrysler, Eaton, and Whirlpool fuzzy logic for use in low-power refrigerators, improved automotive transmissions月nd energy-efficient electric motors.In 1995 Maytag introduced an intelligent dishwasher based on a fuzzy controller and a one-stop sensing module tha

29、t combines a thermistor, for temperature measurement; a conductivity sensor, to measure detergent levelfrom the ions present in the wash; a turbidity sensor that measures scattered and transmitted light to measure the soiling of the wash; and a magnetostrictive sensor to read spin rate. The system d

30、etermines the optimum wash cycle for any load to obtain the best results with the least amount of energy, detergent, and water.Research and development is also continuing on fuzzy applications in software, as opposed to firmware, design, including fuzzy expert systems and integration of fuzzy logic

31、with neural-network and so-called adaptive genetic software systems, with the ultimate goal of building self-learning fuzzy control systems.Fuzzy setsThe input variables in a fuzzy control system are in general mapped into by setsof membership functions similar to this, known as fuzzy sets. The proc

32、ess of converting a crisp input value to a fuzzy value is called fuzzification.A control system may also deal with them as simplified fuzzy functions that to be either one value or another.Given mappings of input variables into membership functions and truth values, the microcontroller then makes de

33、cisionsfor what action to take based on a set of rules, each of the form.In one example, the two input variables are brake temperature and speed that .This rule by itself is very puzzling since it looks like it could be used without bothering with fuzzy logic, but remember that the decisionis based

34、on a set of rules:All the rules that apply are invoked, using the membership functions and truth values obtained from the inputs, to determine the result of the rule.This result in turn will be mapped into a membership function and truth value controlling the output variable.These results are combin

35、ed to give a specific (crisp) answer, the actual brake pressure, a procedure known as defuzzification. This combination of fuzzy operations and rule-based inference describes a fuzzy expert system.Traditional control systemsare based on mathematical models in which the control system is described us

36、ing one or more differential equations that define the system response to its inputs. Such systems are oftenimplementedasPIDcontrollers(proportional-integral-derivative controllers). They are the products of decades of development and theoretical analysis, and are many cases, the mathematical model

37、of the control process may not exist, or may betoo expensive in terms of computer processingpower and memory, and a system based on empirical rules may be more effective. Furthermore, fuzzy logic is well suited to low-cost implementations based on cheap sensors, low-resolution analog-to-digital conv

38、erters, and 4-bit or 8-bit one-chip microcontroller chips. Such systems can be easily upgraded by adding new rules to improve performance or add new features. In many cases,fuzzy control can be used to improve existing traditional controller systems by adding an extra layer of intelligence to the cu

39、rrent control method.Fuzzy control in detailFuzzy controllers are very simple conceptually.They consistof an input stage, a processingstage, and an output stage. The input stage maps sensor or other inputs, such as switches, thumbwheels, and so on, to the appropriate membership functions and truth v

40、alues. The processingstage invokes each appropriate rule and generatesa result for each, then combines the results of the rules. Finally, the output stage converts the combined result back into a specific control output value.The most common shape of membership functions is triangular, although trap

41、ezoidal and bell curves are also used, but the shape is generally less important than the number of curves and their placement. From three to seven curves are generally appropriate to cover the required range of an input value, or the universe of discourse in fuzzy jargon.As discussed earlier, the p

42、rocessing stage is based on a collection of logic rules in the form of IF-THEN statements, where the IF part is called the antecedent and the THEN part is called the consequent.This rule uses the truth value of the temperature input, which is some truth value of cold, to generate a result in the fuz

43、zy set for the that the output itself will be set to some cases, the membership functions can be modified by vary considerably between different implementations. Very, for one example, squares membership functions; since the membership values are always less than 1, this narrows the membership funct

44、ion. Extremely cubes the values to give greater narrowing, while somewhat broadens the function by taking the square root.In practice, the fuzzy rule sets usually the definitions tend to vary: AND, in one popular definition, simply uses the minimum weight of all the antecedents, while OR uses the ma

45、ximum value. There is also a NOT operator that subtracts a membership function from 1 to give the complementary function.There are several ways to define the result of a rule, but one of the most common and simplest is the max-min inference method, in which the output membership function is given th

46、e truth value generated by the premise.Rules can be solved in parallel in software. The results of all the rules that theory, each with various advantages and drawbacks.The centroid method is very popular, in which the center of mass of the result provides the crisp value. Another approach is the in

47、ferring and centroid defuzzification for a system with input variables x, y, and z and an output variable n. Note that mu is standard fuzzy-logic nomenclature for truth value:Fuzzy control system design is based on empirical methods, basically a methodical approach to trial-and-error. The general pr

48、ocess is as follows:1. Document the systems operational specifications and inputs and outputs.2. Document the fuzzy sets for the inputs.3. Document the rule set.4. Determine the defuzzification method.5. Run through test suite to validate system, adjust details as required.6plete document and releas

49、e to production.Logical interpretation of fuzzy controlIn spite of the appearance there are several difficulties to give a rigorous logical interpretation of the IF-THEN rules. As an example, interpret a rule as IF (temperature is cold) THEN ( input such that Cold(r) is false. Then the formula Cold(

50、r) f High(t) is true for any t and therefore any t gives a correct control given r. Obviously, if we consider systems of rules in which the class antecedent define a partition such a paradoxical phenomenon does not arise. In any case it is sometimes unsatisfactory to consider two variables x and y i

51、n a rule without some kind of functional dependence. A rigorouslogical justification of fuzzycontrol is given in H jeksdwok ,where fuzzy control is represented as a theory of H j(eks basic logic. Also in Gerla 2005 a logical approach to fuzzy control is proposedbased on the following idea. Denote by

52、 f the fuzzy function associatedwith the fuzzy control system,i.e., given the input r, s(y) = f(r,y) is the fuzzy set of possibleoutputs. Then given a possible output t, we interpret f(r,t) as the truth degree of the claim t is a good answer given r. More formally, any system of IF-THEN rules can be

53、 translate into a fuzzy program in such a way that the fuzzy function f is the interpretation of a vague predicate Good(x,y) in the associatedleast fuzzy Herbrand model. In such a way fuzzy control becomesa chapter of fuzzy logic programming. The learning process becomes a question belonging to indu

54、ctive logic theory.选择我们，选择成功！如需备注，请自行写在下面：单片机毕业设计题目，电子毕业设计题目1. 单片机接入Internet技术在智能小区中的应用与研究2. 基于PIC单片机的高压智能同步开关控制系统设计3. 基于单片机的刚性转子现场动平衡测试系统的研制4. 基于单片机的现场多道核能谱数据采集系统研究5. 单片机模糊控制晶闸管直流调压系统的研究6. 单片机嵌入式TCPIP协议的研究与实现7. 基于单片机的几何参数主动量仪和通用测控仪的研制8. 基于C8051单片机的足球机器人小车控制系统设计9. 使用FPGA模拟实现8051单片机及其外设的功能10. 用于TDMol

55、P实现的E ,1功能卡单片机控制研究11. 基于MSP430单片机的数字式压力表的设计与实现12. 基于CAN总线的单片机流量控制系统的研究13. 单片机和嵌入式系统开发平台化的研究14. 基于单片机语音识别系统设计15. 基于80C佃6KC单片机的舞蹈机器人控制系统16. 基于单片机的工业缝纫机控制系统研制17. 基于单片机的智能稳压电源18. PIC单片机中国市场拓展战略佃.基于FPGA与单片机的高精度电子经纬仪光电信号处理系统研制20. 基于网络单片机的嵌入式远程监控系统研究21. 基于“单片机+CPLDFPGA体系结构”的程控交换机系统集成化设计22. 智能温室环境控制系统的设计与试验

56、研究一一单片机信号采集及其通信控制 系统研究部分23. 弧焊逆变电源单片机控制系统的稳定性研究24. 单片机系统仿真一对用户的软、硬件系统运行过程仿真25. 单片机系统仿真一生成用户硬件电路和汇编语言程序的故障诊断26. 单片机嵌入TCPIP的研究与实现27. 雷达模拟器中的单片机应用28. 基于单片机的沥青摊铺机自动调平控制器的研究29. 单片机控制逆变埋弧焊机系统设计30. 基于sx52单片机的web服务器的设计与实现31. 基于VHDL语言的单片机设计32.单片机实现的仿人智能PID控制器33. 基于单片机的船舶柴油机冷却水温度控制系统34. 基于单片机的活性炭测氡仪的研制35. 单片机

57、静脉麻醉靶控输注系统的研制与应用36. 基于PC+单片机的环境风洞风速控制系统的研究37. 基于CPLD和单片机的爆轰波数据采集系统设计38. 基于单片机和DSP的卷绕控制器数据采集和通讯设计39. 基于MSP430单片机的柴油发电机监控器的设计40. 基于CPLDFPGA和单片机的爆速仪设计41. 基于单片机控制的晶闸管中频感应电源的研制42. 基于十六位单片机的电力设备故障在线监测装置的设计与算法研究43. 基于SPCE061A单片机的语音识别系统的研究44. 基于PIC单片机的生物机能实验装置的研究45. 基于Motorola MC68HC08 系列单片机演示系统的设计与实现46. 基于TCPIP协议的单片机与INTERNET互连的设计与实现47. 基于嵌入式实时操作系统和TCPIP协议的单片机测控系统48. AVR 8位嵌入式单片机在车载