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Neuro-Fuzzy Architectures and Hybrid Learning

  • Book
  • © 2002

Overview

Authors:
  1. Danuta Rutkowska

    1. Department of Computer Engineering, Technical University of Czestochowa, Czestochowa, Poland

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  • Novel neuro-fuzzy architectures and hybrid learning algorithms
  • Overview of early and latest results concerning neural networks and fuzzy sets and systems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 85)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    • Danuta Rutkowska
    Pages 1-3

  3. Description of Fuzzy Inference Systems

    • Danuta Rutkowska
    Pages 5-67

  4. Neural Networks and Neuro-Fuzzy Systems

    • Danuta Rutkowska
    Pages 69-103

  5. Neuro-Fuzzy Architectures Based on the Mamdani Approach

    • Danuta Rutkowska
    Pages 105-126

  6. Neuro-Fuzzy Architectures Based on the Logical Approach

    • Danuta Rutkowska
    Pages 127-163

  7. Hybrid Learning Methods

    • Danuta Rutkowska
    Pages 165-207

  8. Intelligent Systems

    • Danuta Rutkowska
    Pages 209-228

  9. Summary

    • Danuta Rutkowska
    Pages 229-231

  10. Back Matter

    Pages 233-288
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Keywords

About this book

The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe­ matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific progress is being driven by a quest for machine intelligence - for systems which possess a high MIQ (Machine IQ) and can perform a wide variety of physical and mental tasks with minimal human intervention. The role model for intelligent systems is the human mind. The influ­ ence of the human mind as a role model is clearly visible in the methodolo­ gies which have emerged, mainly during the past two decades, for the con­ ception, design and utilization of intelligent systems. At the center of these methodologies are fuzzy logic (FL); neurocomputing (NC); evolutionary computing (EC); probabilistic computing (PC); chaotic computing (CC); and machine learning (ML). Collectively, these methodologies constitute what is called soft computing (SC). In this perspective, soft computing is basically a coalition of methodologies which collectively provide a body of concepts and techniques for automation of reasoning and decision-making in an environment of imprecision, uncertainty and partial truth.

Authors and Affiliations

  • Department of Computer Engineering, Technical University of Czestochowa, Czestochowa, Poland

    Danuta Rutkowska

Bibliographic Information

  • Book Title: Neuro-Fuzzy Architectures and Hybrid Learning

  • Authors: Danuta Rutkowska

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1802-4

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2002

  • Hardcover ISBN: 978-3-7908-1438-5Published: 14 December 2001

  • Softcover ISBN: 978-3-7908-2500-8Published: 21 October 2010

  • eBook ISBN: 978-3-7908-1802-4Published: 13 November 2012

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XIII, 288

  • Topics: Artificial Intelligence

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