| Titre : | Neuro-fuzzy and soft computing : a computational approach to learning and machine intelligence | | Type de document : | texte imprimé | | Auteurs : | Jyh-Shing Roger Jang, Auteur ; Chuen-Tsai Sun, Auteur ; Eiji Mizutani, Auteur | | Editeur : | New Jersey : Prentice-Hall | | Année de publication : | 1997 | | Importance : | 614 p. | | Présentation : | couv. ill. en coul., ill. | | Format : | 23 cm. | | ISBN/ISSN/EAN : | 978-0-13-261066-7 | | Langues : | Anglais (eng) | | Index. décimale : | 08-06 Algorithme | | Résumé : | Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk. | | Note de contenu : | Table of contents
1. Introduction to Neuro-Fuzzy and Soft Computing.
I. FUZZY SET THEORY.
2. Fuzzy Sets.
3. Fuzzy Rules and Fuzzy Reasoning.
4. Fuzzy Inference Systems.
II. REGRESSION AND OPTIMIZATION.
5. Least-Squares Methods for System Identification.
6. Derivative-Based Optimization.
7. Derivative-Free Optimization.
III. NEURAL NETWORKS.
8. Adaptive Networks.
9. Supervised Learning Neural Networks.
10. Learning from Reinforcement.
11. Unsupervised Learning and Other Neural Networks.
IV. NEURO-FUZZY MODELING.
12. ANFIS: Adaptive-Networks-based Fuzzy Inference Systems.
13. Coactive Neuro-Fuzzy Modeling: Towards Generalized ANFIS.
V. ADVANCED NEURO-FUZZY MODELING.
14. Classification and Regression Trees.
15. Data Clustering Algorithms.
16. Rulebase Structure Identification.
VI. NEURO-FUZZY CONTROL.
17. Neuro-Fuzzy Control I.
18. Neuro-Fuzzy Control II.
VII. ADVANCED APPLICATIONS.
19. ANFIS Applications.
20. Fuzzy-Filtered Neural Networks.
21. Fuzzy Theory and Genetic Algorithms in Game Playing.
22. Soft Computing for Color Recipe Prediction.
A Hints to select exercices
B List to Internet resources
C List of MATLAB Programs |
Neuro-fuzzy and soft computing : a computational approach to learning and machine intelligence [texte imprimé] / Jyh-Shing Roger Jang, Auteur ; Chuen-Tsai Sun, Auteur ; Eiji Mizutani, Auteur . - New Jersey : Prentice-Hall, 1997 . - 614 p. : couv. ill. en coul., ill. ; 23 cm. ISBN : 978-0-13-261066-7 Langues : Anglais ( eng) | Index. décimale : | 08-06 Algorithme | | Résumé : | Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an "Internet Resource Page" to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk. | | Note de contenu : | Table of contents
1. Introduction to Neuro-Fuzzy and Soft Computing.
I. FUZZY SET THEORY.
2. Fuzzy Sets.
3. Fuzzy Rules and Fuzzy Reasoning.
4. Fuzzy Inference Systems.
II. REGRESSION AND OPTIMIZATION.
5. Least-Squares Methods for System Identification.
6. Derivative-Based Optimization.
7. Derivative-Free Optimization.
III. NEURAL NETWORKS.
8. Adaptive Networks.
9. Supervised Learning Neural Networks.
10. Learning from Reinforcement.
11. Unsupervised Learning and Other Neural Networks.
IV. NEURO-FUZZY MODELING.
12. ANFIS: Adaptive-Networks-based Fuzzy Inference Systems.
13. Coactive Neuro-Fuzzy Modeling: Towards Generalized ANFIS.
V. ADVANCED NEURO-FUZZY MODELING.
14. Classification and Regression Trees.
15. Data Clustering Algorithms.
16. Rulebase Structure Identification.
VI. NEURO-FUZZY CONTROL.
17. Neuro-Fuzzy Control I.
18. Neuro-Fuzzy Control II.
VII. ADVANCED APPLICATIONS.
19. ANFIS Applications.
20. Fuzzy-Filtered Neural Networks.
21. Fuzzy Theory and Genetic Algorithms in Game Playing.
22. Soft Computing for Color Recipe Prediction.
A Hints to select exercices
B List to Internet resources
C List of MATLAB Programs |
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