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Introduction to Neuro-Fuzzy Systems

by Robert Fuller
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Current price ₹3,633.00
Original price ₹5,589.00
Original price ₹5,589.00
Original price ₹5,589.00
(-35%)
₹3,633.00
Current price ₹3,633.00

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Book cover type: Paperback
  • ISBN13: 9783790812565
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Physica-Verlag
  • Publication Date:
  • Pages: 289
  • Original Price: EUR 49.99
  • Language: English
  • Edition: 2000
  • Item Weight: 468 grams
  • BISAC Subject(s): Artificial Intelligence / General, Operations Research, and Management Information Systems

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.

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