模糊逻辑工具箱

Glossary术语表

Adaptive Neuro-Fuzzy Inference System

(ANFIS) A technique for automatically tuning Sugeno-type inference systems based on training data.

Foreword（前言）

The past few years have witnessed a rapid growth in the number and variety of applications of fuzzy logic. The applications range from consumer products such as cameras, camcorders, washing machines, and microwave ovens to industrial process control, medical instrumentation, decision-support systems, and portfolio selection.

过去几年间，模糊逻辑无论是在应用数量上还是应用种类上都呈现快速增长的趋势。其应用范围从消费产品，例如照相机，便携式摄像机，洗衣机，及微波炉到工业过程控制，医药器具，决策支持系统，以及部长职务选举等

To understand the reasons for the growing use of fuzzy logic it is necessary, first, to clarify what is meant by fuzzy logic.

为了理解模糊逻辑为何能得以如此快速使用，首先，有必要理清什么是模糊逻辑。

Fuzzy logic has two different meanings. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. But in a wider sense, which is in predominant use today, fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects with unsharp boundaries in which membership is a matter of degree. In this perspective, fuzzy logic in its narrow sense is a branch of FL. What is important to recognize is that, even in its narrow sense, the agenda of fuzzy logic is very different both in spirit and substance from the agendas of traditional multivalued logical systems.

模糊逻辑有两种含义。从狭义上来说，模糊逻辑是一个逻辑系统，它是一种多值逻辑的扩充。但是广义上来说，就今天多数使用情况来看，模糊逻辑（FL）几乎与模糊理论的同义，这里模糊理论是一种与事物无明显界限的的分类，在这个界限里关系是一个度的问题。以此观点，狭义上说，模糊逻辑应该是模糊逻辑理论（FL）的一个分支。重要的是，我们要认清楚模糊逻辑与以往传统的多值逻辑系统无论是在精神还是主旨上都不相同，甚至从狭义角度也不相同。

In the Fuzzy Logic Toolbox, fuzzy logic should be interpreted as FL, that is, fuzzy logic in its wide sense. The basic ideas underlying FL are explained very clearly and insightfully in the Introduction. What might be added is that the basic concept underlying FL is that of a linguistic variable, that is, a variable whose values are words rather than numbers. In effect, much of FL may be viewed as a methodology for computing with words rather than numbers. Although words are inherently less precise than numbers, their use is closer to human intuition. Furthermore, computing with words exploits the tolerance for imprecision and thereby lowers the cost of solution.

在模糊逻辑工具箱，模糊逻辑应该理解为FL，即模糊逻辑的广义定义。在介绍中，FL的潜

在的基本的意义已经解释的非常清楚和明白。这里，需要增加说明的是，FL潜在的基本含义是一种关于语言上的变量，也就是说，一种既有数字又有语句的变量。事实上，绝大多数，FL在计算机处理中作为方式逻辑通常被视为语句而不是数字。尽管在精度上，语句与生俱来就比数字低，但是其使用更接近人类的直觉认识。另外，语句的计算处理开拓了imprecision 的容度，因此也减小了解决费用。

Another basic concept in FL, which plays a central role in most of its applications, is that of a fuzzy if-then rule or, simply, fuzzy rule. Although rule-based systems have a long history of use in AI, what is missing in such systems is a machinery for dealing with fuzzy consequents and/or fuzzy antecedents. In fuzzy logic, this machinery is provided by what is called the calculus of fuzzy rules. The calculus of fuzzy rules serves as a basis for what might be called the Fuzzy Dependency and Command Language (FDCL). Although FDCL is not used explicitly in the Fuzzy Logic Toolbox, it is effectively one of its principal constituents. In this connection, what is important to recognize is that in most of the applications of fuzzy logic, a fuzzy logic solution is in reality a translation of a human solution into FDCL.

FL另外一种基础概念就是模糊if-then规则，或者更简单地说，是一种模糊规则，此种情况用的比较多，也扮演了一个中心角色。尽管基于规则的系统用在AI中有很长一段历史，但是，此类系统缺少一个能处理模糊结果与/或者是模糊先例的机器。在模糊逻辑中，这种机器可以由一种被称为模糊规则的计算所提供。模糊规则计算为模糊独立与命令语言（FDCL）提供了基础。尽管，FDCL在模糊逻辑工具箱中不能明确地使用，但是作为原则成分相当有效。在连接中，重要的是，认清模糊逻辑的绝大多数应用，事实上，模糊逻辑的解决方案是一种转换为FDCL的人类解决方式。

What makes the Fuzzy Logic Toolbox so powerful is the fact that most of human reasoning and concept formation is linked to the use of fuzzy rules. By providing a systematic framework for computing with fuzzy rules, the Fuzzy Logic Toolbox greatly amplifies the power of human reasoning. Further amplification results from the use of MATLAB and graphical user interfaces, areas in which The MathWorks has unparalleled expertise.3

大部分人类缘由与概念形成与模糊规则的使用相关联使得模糊逻辑工具箱如此之强大。为计算处理模糊规则而提供了一种系统性的架构，模糊逻辑工具箱大大地增强了人类理由的力量。从matlab及图形用户界面的使用，更深一步增强化的结果，可以从MathWorks中获得。

A trend that is growing in visibility relates to the use of fuzzy logic in combination with neurocomputing and genetic algorithms. More generally, fuzzy logic, neurocomputing, and genetic algorithms may be viewed as the principal constituents of what might be called soft computing. Unlike the traditional, hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, and low solution cost. In coming years, soft computing is likely to play an increasingly important role in the conception and design of systems whose MIQ (Machine IQ) is much higher than that of systems designed by conventional methods.

有一种趋势是模糊逻辑与神经计算及遗传算法的结合使用。更一般地，模糊逻辑，神经元计算及遗传算法可以被视为原则成分，一种被称为软计算的成分。不像传统的硬计算，软计算的目标是实现与现实世界的普遍深入的映射。软计算的指导性原则是:开拓映射的容度，不确定性，及部分真实性然后获得易处理性、鲁棒性及较低的解决费用。在接下来的几年里，在MIQ比习惯性方式设计的系统高的多，软计算可能扮演越来越重要的角色。

Among various combinations of methodologies in soft computing, the one that has highest