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Computational Intelligence Systems and Applications

Neuro-Fuzzy and Fuzzy Neural Synergisms

  • Book
  • © 2002

Overview

Authors:
  1. Marian B. Gorzałczany

    1. Department of Electrical and Computer Engineering, Kielce University of Technology, Kielce, Poland

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  • Self-contained presentation of new concepts and structures of CI systems and their real-life applications
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 86)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
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  2. Introduction

    • Marian B. Gorzałczany
    Pages 1-15

  3. Elements of the theory of fuzzy sets

    • Marian B. Gorzałczany
    Pages 17-51

  4. Essentials of artificial neural networks

    • Marian B. Gorzałczany
    Pages 53-84

  5. Brief introduction to genetic algorithms

    • Marian B. Gorzałczany
    Pages 85-101

  6. Main directions of combining artificial neural networks, fuzzy sets and evolutionary computations in designing computational intelligence systems

    • Marian B. Gorzałczany
    Pages 103-125

  7. Neuro-fuzzy(-genetic) system for synthesizing rule-based knowledge from data

    • Marian B. Gorzałczany
    Pages 127-190

  8. Rule-based neuro-fuzzy modelling of dynamic system and designing of controllers

    • Marian B. Gorzałczany
    Pages 191-229

  9. Neuro-fuzzy(-genetic) rule-based classifier designed from data for intelligent decision support

    • Marian B. Gorzałczany
    Pages 231-287

  10. Fuzzy neural network for system modelling and control

    • Marian B. Gorzałczany
    Pages 289-314

  11. Fuzzy neural classifier

    • Marian B. Gorzałczany
    Pages 315-330

  12. Back Matter

    Pages 331-364
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Keywords

About this book

Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which significantly contribute to the description of many real­ world problems, both physical systems and processes as well as mechanisms of decision making. Moreover, there are many situations where the expert domain knowledge (the basis for many symbolic AI systems) is not sufficient for the design of intelligent systems, due to incompleteness of the existing knowledge, problems caused by different biases of human experts, difficulties in forming rules, etc. In general, problem knowledge for solving a given problem can consist of an explicit knowledge (e.g., heuristic rules provided by a domain an implicit, hidden knowledge "buried" in past-experience expert) and numerical data. A study of huge amounts of these data (collected in databases) and the synthesizing of the knowledge "encoded" in them (also referred to as knowledge discovery in data or data mining), can significantly improve the performance of the intelligent systems designed.

Authors and Affiliations

  • Department of Electrical and Computer Engineering, Kielce University of Technology, Kielce, Poland

    Marian B. Gorzałczany

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