| Titre : | Fuzzy Model Identification for Control | | Type de document : | texte imprimé | | Auteurs : | Janos Abonyi, Auteur | | Editeur : | Boston, Basel, Berlin : Birkhäuser | | Année de publication : | 2003 | | Importance : | 273 p. | | Présentation : | couv. ill.,ill. | | Format : | 24 cm. | | ISBN/ISSN/EAN : | 978-0-8176-4238-9 | | Langues : | Anglais (eng) | | Catégories : | AUTOMATISME
| | Mots-clés : | Control Algorithms Matlab control control theory dynamical systems fuzzy control intelligent control process control reading complexity | | Index. décimale : | 25-06 Identification et simulation des processus | | Résumé : | This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice.
Key features:
* detailed review of algorithms and approaches developed for modeling and identification for control
* numerous illustrations to facilitate the understanding of ideas and methods presented
*extensive references give a good overview of the current state of identification and control of dynamic systems and fuzzy modeling, and suggest further reading for additional research
* supporting MATLAB and Simulink files, available at the website www.fmt.vein.hu/softcomp, create a computational platform for exploration and illustration of many concepts and algorithms presented in the book.
The book is aimed primarily at researchers, practitioners, and professionals in process control and identification, but it is also accessible to graduate students in electrical, chemical, and process engineering. Technical prerequisites include an undergraduate-level knowledge of control theory and linear algebra. Additional familiarity with fuzzy systems is helpful but not required. | | Note de contenu : | Table of Contents
Preface
1 Introduction
2 Fuzzy Model Structures and their Analysis
3 Fuzzy Models of Dynamical Systems
4 Fuzzy Model Identification
5 Fuzzy Model Based Control
A Process Models Used for Case Studies
-Index |
Fuzzy Model Identification for Control [texte imprimé] / Janos Abonyi, Auteur . - Boston, Basel, Berlin : Birkhäuser, 2003 . - 273 p. : couv. ill.,ill. ; 24 cm. ISBN : 978-0-8176-4238-9 Langues : Anglais ( eng) | Catégories : | AUTOMATISME
| | Mots-clés : | Control Algorithms Matlab control control theory dynamical systems fuzzy control intelligent control process control reading complexity | | Index. décimale : | 25-06 Identification et simulation des processus | | Résumé : | This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice.
Key features:
* detailed review of algorithms and approaches developed for modeling and identification for control
* numerous illustrations to facilitate the understanding of ideas and methods presented
*extensive references give a good overview of the current state of identification and control of dynamic systems and fuzzy modeling, and suggest further reading for additional research
* supporting MATLAB and Simulink files, available at the website www.fmt.vein.hu/softcomp, create a computational platform for exploration and illustration of many concepts and algorithms presented in the book.
The book is aimed primarily at researchers, practitioners, and professionals in process control and identification, but it is also accessible to graduate students in electrical, chemical, and process engineering. Technical prerequisites include an undergraduate-level knowledge of control theory and linear algebra. Additional familiarity with fuzzy systems is helpful but not required. | | Note de contenu : | Table of Contents
Preface
1 Introduction
2 Fuzzy Model Structures and their Analysis
3 Fuzzy Models of Dynamical Systems
4 Fuzzy Model Identification
5 Fuzzy Model Based Control
A Process Models Used for Case Studies
-Index |
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Fuzzy Model Identification for Control / Janos Abonyi
