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Advanced Fuzzy Systems Design and Applications - nouveau livre

ISBN: 9783790815375

Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristi… Plus…

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Advanced Fuzzy Systems Design and Applications. - edition reliée, livre de poche

2002, ISBN: 3790815373

[EAN: 9783790815375], [SC: 4.0], [PU: Berlin Springer Verlag], FUZZY SYSTEMS; EVOLUTIONARY ALGORITHMS; KNOWLEDGE EXTRACTION; INCORPORATION; NEURAL NETWORKS, Gebraucht - Sehr gut Zustand: … Plus…

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications - edition reliée, livre de poche

2002

ISBN: 3790815373

2003 Gebundene Ausgabe Fuzzy Logik - Fuzzy Set, Intelligenz / Künstliche Intelligenz, KI, Künstliche Intelligenz - AI, Neuronales Netz - Neuronaler Computer - Neurocomputer, Information… Plus…

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications - nouveau livre

2002, ISBN: 9783790815375

Physica-Verlag GmbH & Co, Relié, Auflage: 2003 ed. 272 Seiten, Publiziert: 2002-11-18T00:00:01Z, Produktgruppe: Livre, 2.84 kg, Livres anglais et étrangers, Boutiques, Livres, Dictionnair… Plus…

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications. (=Studies in Fuzziness and Soft Computing, 112). - edition reliée, livre de poche

ISBN: 9783790815375

282 S.; Ill. Hardcover

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Détails sur le livre
Advanced Fuzzy Systems Design and Applications
Auteur:
Titre:
ISBN:

3790815373


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.

Informations détaillées sur le livre - Advanced Fuzzy Systems Design and Applications


EAN (ISBN-13): 9783790815375
ISBN (ISBN-10): 3790815373
Version reliée
Livre de poche
Date de parution: 2002
Editeur: Physica-Verlag GmbH & Co
288 Pages
Poids: 0,596 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2007-05-28T21:11:48+02:00 (Paris)
Page de détail modifiée en dernier sur 2024-03-01T20:27:01+01:00 (Paris)
ISBN/EAN: 3790815373

ISBN - Autres types d'écriture:
3-7908-1537-3, 978-3-7908-1537-5
Autres types d'écriture et termes associés:
Auteur du livre: jin
Titre du livre: advanced, applications soft computing, fuzzy systems, soft oft, studies design, fuzzy systeme, jin, design and applications

Données de l'éditeur

Auteur: Yaochu Jin
Titre: Studies in Fuzziness and Soft Computing; Advanced Fuzzy Systems Design and Applications
Editeur: Physica; Physica
272 Pages
Date de parution: 2002-11-18
Heidelberg; DE
Langue: Anglais
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
X, 272 p. 228 illus.

BB; Hardcover, Softcover / Informatik, EDV/Informatik; Computerprogrammierung und Softwareentwicklung; Verstehen; algorithms; artificial neural network; evolution; evolutionary algorithm; knowledge; knowledge discovery; knowledge representation; learning; modeling; multi-objective optimization; neural network; optimization; robot; robotics; simulation; algorithm analysis and problem complexity; Programming Techniques; Artificial Intelligence; Mathematical Logic and Foundations; Data Structures and Information Theory; Algorithms; Künstliche Intelligenz; Mathematik: Logik; Mathematische Grundlagen; Algorithmen und Datenstrukturen; Informationstheorie; BC

1. Fuzzy Sets and Fuzzy Systems.- 1.1 Basics of Fuzzy Sets.- 1.2 Fuzzy Rule Systems.- 1.3 Interpretability of Fuzzy Rule System.- 1.4 Knowledge Processing with Fuzzy Logic.- 2. Evolutionary Algorithms.- 2.1 Introduction.- 2.2 Generic Evolutionary Algorithms.- 2.3 Adaptation and Self-Adaptation in Evolutionary Algorithms.- 2.4 Constraints Handling.- 2.5 Multi-objective Evolution.- 2.6 Evolution with Uncertain Fitness Functions.- 2.7 Parallel Implementations.- 2.8 Summary.- 3. Artificial Neural Networks.- 3.1 Introduction.- 3.2 Feedforward Neural Network Models.- 3.3 Learning Algorithms.- 3.4 Improvement of Generalization.- 3.5 Rule Extraction from Neural Networks.- 3.6 Interaction between Evolution and Learning.- 3.7 Summary.- 4. Conventional Data-driven Fuzzy Systems Design.- 4.1 Introduction.- 4.2 Fuzzy Inference Based Method.- 4.3 Wang-Mendel’s Method.- 4.4 A Direct Method.- 4.5 An Adaptive Fuzzy Optimal Controller.- 4.6 Summary.- 5.Neural Network Based Fuzzy Systems Design.- 5.1 Neurofuzzy Systems.- 5.2 The Pi-sigma Neurofuzzy Model.- 5.3 Modeling and Control Using the Neurofuzzy System.- 5.4 Neurofuzzy Control of Nonlinear Systems.- 5.5 Summary.- 6. Evolutionary Design of Fuzzy Systems.- 6.1 Introduction.- 6.2 Evolutionary Design of Flexible Structured Fuzzy Controller..- 6.3 Evolutionary Optimization of Fuzzy Rules.- 6.4 Fuzzy Systems Design for High-Dimensional Systems.- 6.5 Summary.- 7. Knowledge Discovery by Extracting Interpretable Fuzzy Rules.- 7.1 Introduction.- 7.2 Evolutionary Interpretable Fuzzy Rule Generation.- 7.3 Interactive Co-evolution for Fuzzy Rule Extraction.- 7.4 Fuzzy Rule Extraction from RBF Networks.- 7.5 Summary.- 8. Fuzzy Knowledge Incorporation into Neural Networks.- 8.1 Data and A Priori Knowledge.- 8.2 Knowledge Incorporation in NeuralNetworks for Control.- 8.3 Fuzzy Knowledge Incorporation By Regularization.- 8.4 Fuzzy Knowledge as A Related Task in Learning.- 8.5 Simulation Studies.- 8.6 Summary.- 9. Fuzzy Preferences Incorporation into Multi-objective Optimization.- 9.1 Multi-objective Optimization and Preferences Handling.- 9.2 Evolutionary Dynamic Weighted Aggregation.- 9.3 Fuzzy Preferences Incorporation in MOO.- 9.4 Summary.- References.
Includes supplementary material: sn.pub/extras

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