Advanced Fuzzy Systems Design and Applications

Yaochu Jin

282 pages, parution le 03/12/2002

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Résumé

This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.

Contents

Fuzzy Sets and Fuzzy Systems
Evolutionary Algorithms
Artificial Neural Networks
Conventional Data-driven Fuzzy Systems Design
Neural Network Based Fuzzy Systems Design
Evolutionary Design of Fuzzy Systems
Knowledge Discovery by Extracting Interpretable Fuzzy Rules
Fuzzy Knowledge Incorporation into Neural Networks
Fuzzy Preferences Incorporation into Evolutionary Multi-objective Optimization.

L'auteur - Yaochu Jin

Honda R&D Europe GmbH, Offenbach, Germany

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Yaochu Jin
Parution	03/12/2002
Nb. de pages	282
Format	16 x 24
Couverture	Relié
Poids	553g
Intérieur	Noir et Blanc
EAN13	9783790815375