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Fuzzy and Neuro-Fuzzy Intelligent Systems

  • Book
  • © 2000

Overview

Authors:
  1. Ernest Czogała

    1. Institute of Electronics, Silesian University of Technology, Gliwice, Poland

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  2. Jacek Łęski

    1. Institute of Electronics, Silesian University of Technology, Gliwice, Poland

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    You can also search for this author in PubMed   Google Scholar

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 47)

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Table of contents (7 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Classical sets and fuzzy sets Basic definitions and terminology

    • Ernest Czogała, Jacek Łęski
    Pages 1-26

  3. Approximate reasoning

    • Ernest Czogała, Jacek Łęski
    Pages 27-64

  4. Artificial neural networks

    • Ernest Czogała, Jacek Łęski
    Pages 65-92

  5. Unsupervised learning Clustering methods

    • Ernest Czogała, Jacek Łęski
    Pages 93-127

  6. Fuzzy systems

    • Ernest Czogała, Jacek Łęski
    Pages 129-139

  7. Neuro-fuzzy systems

    • Ernest Czogała, Jacek Łęski
    Pages 141-162

  8. Applications of artificial neural network based fuzzy inference system

    • Ernest Czogała, Jacek Łęski
    Pages 163-180

  9. Back Matter

    Pages 181-196
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Keywords

About this book

Intelligence systems. We perfonn routine tasks on a daily basis, as for example: • recognition of faces of persons (also faces not seen for many years), • identification of dangerous situations during car driving, • deciding to buy or sell stock, • reading hand-written symbols, • discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: • diagnosing diseases, • localizing faults in electronic circuits, • optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.

Authors and Affiliations

  • Institute of Electronics, Silesian University of Technology, Gliwice, Poland

    Ernest Czogała, Jacek Łęski

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