毕业设计(论文)外文文献翻译要求及封面[样例5]

第一篇：毕业设计(论文)外文文献翻译要求及封面

毕业设计（论文）外文文献翻译要求

根据《普通高等学校本科毕业设计（论文）指导》的内容，特对外文文献翻译提出以下要求：

一、翻译的外文文献一般为1～2篇，外文字符要求不少于1.5万（或翻译成中文后至少在3000字以上）。

二、翻译的外文文献应主要选自学术期刊、学术会议的文章、有关著作及其他相关材料，应与毕业论文（设计）主题相关，并作为外文参考文献列入毕业论文（设计）的参考文献。并在每篇中文译文首页用“脚注”形式注明原文作者及出处，中文译文后应附外文原文。

三、中文译文的基本撰写格式为题目采用小三号黑体字居中打印，正文采用宋体小四号字，行间距一般为固定值20磅，标准字符间距。页边距为左3cm，右2.5cm，上下各2.5cm，页面统一采用A4纸。

四、封面格式由学校统一制作（注：封面上的“翻译题目”指中文译文的题目，附件1为一篇外文翻译的封面格式，附件二为两篇外文翻译的封面格式），若有两篇外文文献，请按“封面、译文

一、外文原文

一、译文

二、外文原文二”的顺序统一装订。

教务处

2006年2月27日

杭州电子科技大学

毕业设计（论文）外文文献翻译

毕业设计（论文）题目

翻译题目

学院

专业

姓名

班级

学号

指导教师

杭州电子科技大学

毕业设计（论文）外文文献翻译

毕业设计（论文）题目

翻译（1）题目 翻译（2）题目

学院

专业

姓名

班级

学号

指导教师

第二篇：毕业设计(论文)外文文献翻译要求

毕业设计（论文）外文文献翻译要求

根据《浙江省教育厅高教处关于对高等学校2004届本专科学生毕业设计（论文）进行抽查的通知》的评审要求，“本科毕业论文要求翻译外文文献2篇以上”。为提高毕业论文（设计）的质量，并与教育厅评审要求相一致，经研究决定，2005届毕业论文（设计）要求翻译2篇外文文献，外文字符不少于1.5万, 每篇外文文献翻译的中文字数一般要求2000－3000左右。

翻译的外文文献应主要选自学术期刊、学术会议的文章、有关著作及其他相关材料，应与毕业论文（设计）主题相关，并作为外文参考文献列入毕业论文（设计）的参考文献。并在每篇中文译文首页用“脚注”形式注明原文作者及出处，中文译文后应附外文原文。中文译文的基本撰写格式为题目采用小三号黑体字居中打印，正文采用宋体五号字，行间距一般为固定值20磅，标准字符间距。

湖州师范学院（求真学院）

毕业设计（论文）外文文献翻译

毕业设计（论文）题目

翻译（1）题目

翻译（2）题目

学院 专业 姓名 班级 学号 指导教师

第三篇：4毕业设计(论文)外文文献翻译范文

黄石理工学院毕业设计（论文）外文文献翻译

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1973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,Yamakawa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酒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。

黄石理工学院毕业设计（论文）外文文献翻译

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。1988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“自主学习”模糊控制系统。

黄石理工学院毕业设计（论文）外文文献翻译

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”、“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

黄石理工学院毕业设计（论文）外文文献翻译

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IFThen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(x)→高(y)和假设r是一个输入这样冷(r)是假的。然后公式冷(r)→高(t)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。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x和y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r,s(y)f(r,y)是模糊集合可能的输出。然后给出一个可能的输出的t,我们把f(r,t)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f模糊谓词的解释很好(x,y)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归纳逻辑理论。

黄石理工学院毕业设计（论文）外文文献翻译

Fuzzy Control From Wikipedia November 2011

Overview

Fuzzy logic is widely used in machine control.The term itself inspires a certain skepticism, sounding equivalent to ”half-baked logic“ or ”bogus logic“, but the ”fuzzy“ part does not refer to a lack of rigour in the method, rather to the fact that the logic involved can deal with concepts that cannot be expressed as ”true“ or ”false“ but rather as ”partially true“.Although genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller.This makes it easier to mechanize tasks that are already successfully performed by humans.History and applications

Fuzzy logic was first proposed by Lotfi A.Zadeh of the University of California at Berkeley in a 1965 paper.He elaborated on his ideas in 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 industrial application, a cement kiln built in Denmark, coming on line in 1975.Fuzzy systems were largely ignored in the U.S.because they were associated with 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 not have this prejudice.Interest in 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 the line opened in 1987.Another event in 1987 helped promote interest in fuzzy systems.During an international meeting of fuzzy researchers in 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 control problem, in which a vehicle tries to keep a pole mounted on its top by a hinge upright by moving back and forth.Observers were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine glass containing water or even a live mouse to the top of the pendulum.The system maintained stability in both cases.Yamakawa eventually went on to organize his own fuzzy-systems research lab to help exploit his patents in the field.Following such demonstrations, Japanese engineers developed a wide range of fuzzy systems for both industrial and consumer applications.In 1988 Japan established

黄石理工学院毕业设计（论文）外文文献翻译

the Laboratory for International Fuzzy Engineering(LIFE), a cooperative arrangement between 48 companies to pursue fuzzy research.Matsushita vacuum 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 detergent.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 controls its speed to prevent overshoot.The camera's 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 kilobytes of memory.As another example of a practical system, an industrial air conditioner designed by Mitsubishi uses 25 heating rules and 25 cooling rules.A temperature sensor provides input, with control outputs fed to an inverter, a compressor valve, and a fan motor.Compared to the previous design, the fuzzy controller heats and cools five times faster, reduces power consumption by 24%, increases temperature stability by a factor of two, and uses fewer sensors.The enthusiasm of the Japanese for fuzzy logic is reflected in the wide range of other applications they have investigated or implemented: character and handwriting recognition;optical fuzzy systems;robots, voice-controlled robot helicopters Work on fuzzy systems is also proceeding in the US and Europe.The US Environmental Protection Agency has investigated fuzzy control for energy-efficient motors, and NASA has studied fuzzy control for automated space docking: simulations show that a fuzzy control system can greatly reduce fuel consumption.Firms such as Boeing, General Motors, Allen-Bradley, Chrysler, Eaton, and Whirlpool have worked on fuzzy logic for use in low-power refrigerators, improved automotive transmissions, and energy-efficient electric motors.In 1995 Maytag introduced an ”intelligent“ dishwasher based on a fuzzy controller and a ”one-stop sensing module“ that combines a thermistor, for temperature measurement;a conductivity sensor, to measure detergent level from 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 determines 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 with neural-network and so-called adaptive ”genetic“ software systems, with the ultimate goal of building ”self-learning“ fuzzy control systems.黄石理工学院毕业设计（论文）外文文献翻译

Fuzzy sets

The input variables in a fuzzy control system are in general mapped into by sets of membership functions similar to this, known as ”fuzzy sets“.The process of converting a crisp input value to a fuzzy value is called ”fuzzification“.A control system may also have various types of switch, or ”ON-OFF“, inputs along with its analog inputs, and such switch inputs of course will always have a truth value equal to either 1 or 0, but the scheme can deal with them as simplified fuzzy functions that happen to be either one value or another.Given ”mappings“ of input variables into membership functions and truth values, the microcontroller then makes decisions for 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 have values defined as fuzzy sets.The output variable, ”brake pressure“, is also defined by a fuzzy set that can have values like ”static“, ”slightly increased“, ”slightly decreased“, and so on.This rule by itself is very puzzling since it looks like it could be used without bothering with fuzzy logic, but remember that the decision is based 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 combined 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 systems are based on mathematical models in which the control system is described using one or more differential equations that define the system response to its inputs.Such systems are often implemented as ”PID controllers“(proportional-integral-derivative controllers).They are the products of decades of development and theoretical analysis, and are highly effective.If PID and other traditional control systems are so well-developed, why bother with fuzzy control? It has some advantages.In many cases, the mathematical model of the control process may not exist, or may be too ”expensive“ in terms of computer processing power 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 converters, 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 current control method.黄石理工学院毕业设计（论文）外文文献翻译

Fuzzy control in detail

Fuzzy controllers are very simple conceptually.They consist of an input stage, a processing stage, 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 values.The processing stage invokes each appropriate rule and generates a 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 trapezoidal 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 processing 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 fuzzy set for the ”heater“ output, which is some value of ”high“.This result is used with the results of other rules to finally generate the crisp composite output.Obviously, the greater the truth value of ”cold“, the higher the truth value of ”high“, though this does not necessarily mean that the output itself will be set to ”high“ since this is only one rule among many.In some cases, the membership functions can be modified by ”hedges“ that are equivalent to adjectives.Common hedges include ”about“, ”near“, ”close to“, ”approximately“, ”very“, ”slightly“, ”too“, ”extremely“, and ”somewhat“.These operations may have precise definitions, though the definitions can 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 function.”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 have several antecedents that are combined using fuzzy operators, such as AND, OR, and NOT, though again the definitions tend to vary: AND, in one popular definition, simply uses the minimum weight of all the antecedents, while OR uses the maximum 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 the truth value generated by the premise.Rules can be solved in parallel in hardware, or sequentially in software.The results of all the rules that have fired are ”defuzzified“ to a crisp value by one of several methods.There are dozens in 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 ”height“ method, which takes the value of the biggest contributor.The centroid method favors the rule with the output of

黄石理工学院毕业设计（论文）外文文献翻译

greatest area, while the height method obviously favors the rule with the greatest output value.The diagram below demonstrates max-min inferring 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 process is as follows:

1.Document the system's 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.6.Complete document and release to production.Logical interpretation of fuzzy control In 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(heater is ”high“)by the first order formula Cold(x)→High(y)and assume that r is an input such that Cold(r)is false.Then the formula Cold(r)→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 in a rule without some kind of functional dependence.A rigorous logical justification of fuzzy control is given in Hájek's book ,where fuzzy control is represented as a theory of Hájek's basic logic.Also in Gerla 2005 a logical approach to fuzzy control is proposed based on the following idea.Denote by f the fuzzy function associated with the fuzzy control system, i.e., given the input r, s(y)= f(r,y)is the fuzzy set of possible outputs.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 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 associated least fuzzy Herbrand model.In such a way fuzzy control becomes a chapter of fuzzy logic programming.The learning process becomes a question belonging to inductive logic theory.

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