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Evolving Rule-Based Models

A Tool for Design of Flexible Adaptive Systems

  • Book
  • © 2002

Overview

Authors:
  1. Plamen P. Angelov

    1. Department of Civil and Building Engineering, Loughborough University, Loughborough, UK

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  • First approach to on-line generation and adaptation of rule-based models
  • Illustration with real engineering examples
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 92)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction

    1. Introduction

      • Plamen P. Angelov
      Pages 1-9

  3. System Modelling: Basic Principles

    1. Front Matter

      Pages 11-11
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    2. Conventional Models

      • Plamen P. Angelov
      Pages 13-24

    3. Flexible Models

      • Plamen P. Angelov
      Pages 25-42

  4. Flexible Models Identification

    1. Front Matter

      Pages 43-46
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    2. Non-Linear Approach to (Off-line) Identification of Flexible Models

      • Plamen P. Angelov
      Pages 47-65

    3. Quasi-Linear Approach to FRB Models (Off-Line) Identification

      • Plamen P. Angelov
      Pages 67-77

    4. Intelligent and Smart Adaptive Systems

      • Plamen P. Angelov
      Pages 79-85

    5. On-Line Identification of Flexible TSK-Type Models

      • Plamen P. Angelov
      Pages 87-109

  5. Engineering Applications

    1. Front Matter

      Pages 111-113
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    2. Modelling Indoor Climate Control Systems

      • Plamen P. Angelov
      Pages 115-167

    3. On-Line Modelling of Fermentation Processes

      • Plamen P. Angelov
      Pages 169-179

    4. Intelligent Risk Assesment

      • Plamen P. Angelov
      Pages 181-192

    5. Conclusions

      • Plamen P. Angelov
      Pages 193-197

  6. Back Matter

    Pages 199-213
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Keywords

About this book

The idea about this book has evolved during the process of its preparation as some of the results have been achieved in parallel with its writing. One reason for this is that in this area of research results are very quickly updated. Another is, possibly, that a strong, unchallenged theoretical basis in this field still does not fully exist. From other hand, the rate of innovation, competition and demand from different branches of industry (from biotech industry to civil and building engineering, from market forecasting to civil aviation, from robotics to emerging e-commerce) is increasingly pressing for more customised solutions based on learning consumers behaviour. A highly interdisciplinary and rapidly innovating field is forming which focus is the design of intelligent, self-adapting systems and machines. It is on the crossroads of control theory, artificial and computational intelligence, different engineering disciplines borrowing heavily from the biology and life sciences. It is often called intelligent control, soft computing or intelligent technology. Some other branches have appeared recently like intelligent agents (which migrated from robotics to different engineering fields), data fusion, knowledge extraction etc., which are inherently related to this field. The core is the attempts to enhance the abilities of the classical control theory in order to have more adequate, flexible, and adaptive models and control algorithms.

Authors and Affiliations

  • Department of Civil and Building Engineering, Loughborough University, Loughborough, UK

    Plamen P. Angelov

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