Introduction to Neuro-Fuzzy Systems

Introduction to Neuro-Fuzzy Systems Synopsis

Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic pro- vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associ- ated with human cognitive processes, such as thinking and reasoning.

The conventional approaches to knowledge representation lack the means for rep- resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of com- monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision.

Some of the essential characteristics of fuzzy logic relate to the following [242]. In fuzzy logic, exact reasoning is viewed as a limiting case of ap- proximate reasoning. In fuzzy logic, everything is a matter of degree.

In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. Inference is viewed as a process of propagation of elastic con- straints. Any logical system can be fuzzified.

There are two main characteristics of fuzzy systems that give them better performance für specific applications.