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Introduction to Neuro-Fuzzy Systems

  • Textbook
  • © 2000

Overview

Authors:
  1. Robert Fullér

    1. Department of Operations Research, Eötvös Lorànd University, Budapest, Hungary
      Institute of Advanced Management Systems Research, Åbo Akademi University, Turku, Finland

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  • Contains numerous exercises with solutions
  • Starts from the basics of fuzzy sets and neural nets then provides a broad overview of integrated approaches

Part of the book series: Advances in Intelligent and Soft Computing (AINSC, volume 2)

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Table of contents (4 chapters)

  1. Front Matter

    Pages I-XII
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  2. Fuzzy systems

    • Robert Fullér
    Pages 1-131

  3. Artificial neural networks

    • Robert Fullér
    Pages 133-170

  4. Fuzzy neural networks

    • Robert Fullér
    Pages 171-254

  5. Appendix

    • Robert Fullér
    Pages 255-286

  6. Back Matter

    Pages 287-289
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Keywords

About this book

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.

Authors and Affiliations

  • Department of Operations Research, Eötvös Lorànd University, Budapest, Hungary

    Robert Fullér

  • Institute of Advanced Management Systems Research, Åbo Akademi University, Turku, Finland

    Robert Fullér

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