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Knowledge-Based Neurocomputing: A Fuzzy Logic Approach

  • Eyal Kolman
  • Michael Margaliot

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 234)

Table of contents

  1. Front Matter
  2. Eyal Kolman, Michael Margaliot
    Pages 1-12
  3. Eyal Kolman, Michael Margaliot
    Pages 13-19
  4. Eyal Kolman, Michael Margaliot
    Pages 21-35
  5. Eyal Kolman, Michael Margaliot
    Pages 37-40
  6. Eyal Kolman, Michael Margaliot
    Pages 41-57
  7. Eyal Kolman, Michael Margaliot
    Pages 59-76
  8. Eyal Kolman, Michael Margaliot
    Pages 77-81
  9. Back Matter

About this book

Introduction

In this monograph, the authors introduce a novel fuzzy rule-base, referred to as the Fuzzy All-permutations Rule-Base (FARB). They show that inferring the FARB, using standard tools from fuzzy logic theory, yields an input-output map that is mathematically equivalent to that of an artificial neural network. Conversely, every standard artificial neural network has an equivalent FARB.

The FARB-ANN equivalence integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based neurocomputing in artificial neural networks.

Keywords

All-Permutations Fuzzy Rule Base Equivalence Fuzzy Fuzzy Rule-Based Systems Knowedge Refinement Knowledge Extraction Knowledge Insertion Knowledge-Based Neurocomputing cognition fuzzy logic

Authors and affiliations

  • Eyal Kolman
    • 1
  • Michael Margaliot
    • 1
  1. 1.Tel Aviv UniversityTel AvivIsrael

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-88077-6
  • Copyright Information Springer-Verlag Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-88076-9
  • Online ISBN 978-3-540-88077-6
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site