Introduction to Neuro-Fuzzy Systems (Advances in Intelligent and Soft Computing, 2) - Softcover

Fuller, Robert

Softcover

ISBN 10: 3790812560 ISBN 13: 9783790812565

Publisher: Physica, 1999

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.

About this edition

• Publisher: Physica
• Publication date: 1999
• ISBN 10: 3790812560
• ISBN 13: 9783790812565
• Binding: Paperback
• Number of pages: 301