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Item Details
| Title:
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INTRODUCTION TO NEURO-FUZZY SYSTEMS
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| By: |
Robert Fuller |
| Format: |
Paperback |
| List price:
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£89.99 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
3790812560 |
| ISBN 13: |
9783790812565 |
| Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
| Pub. date: |
17 November, 1999 |
| Edition: |
2000 ed. |
| Series: |
Advances in Intelligent and Soft Computing 2 |
| Pages: |
289 |
| Description: |
This guide introduces the basics of neuro-fuzzy systems. Its main purpose is to explain the information processing methods most widely used in fuzzy inference systems, neural networks and neuro-fuzzy systems. Diagrams and numerical exercises with solutions help explain the principles involved. |
| 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 fur specific applications. |
| Illustrations: |
11 Tables, black and white; XII, 289 p. |
| Publication: |
Germany |
| Imprint: |
Physica-Verlag GmbH & Co |
| Returns: |
Returnable |
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