Evolving Rule-Based Models

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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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.

• algorithms
• complex system
• complex systems
• decision support system
• fuzzy
• genetic algorithms
• model
• modeling
• system
• complexity

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

  Plamen P. Angelov

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