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Advanced Fuzzy Systems Design and Applications
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Advanced Fuzzy Systems Design and Applications - libro nuevo

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… Más…

Nr. 978-3-7908-1537-5. Gastos de envío:Worldwide free shipping, , DE. (EUR 0.00)
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Advanced Fuzzy Systems Design and Applications. - Yaochu Jin
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Yaochu Jin:

Advanced Fuzzy Systems Design and Applications. - encuadernado, tapa blanda

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: … Más…

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications - encuadernado, tapa blanda

2002

ISBN: 3790815373

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

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications - libro nuevo

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… Más…

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Jin, Yaochu:
Advanced Fuzzy Systems Design and Applications. (=Studies in Fuzziness and Soft Computing, 112). - encuadernado, tapa blanda

ISBN: 9783790815375

282 S.; Ill. Hardcover

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Detalles del libro
Advanced Fuzzy Systems Design and Applications
Autor:
Título:
ISBN:

9783790815375


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.

Detalles del libro - Advanced Fuzzy Systems Design and Applications


EAN (ISBN-13): 9783790815375
ISBN (ISBN-10): 3790815373
Tapa dura
Tapa blanda
Año de publicación: 2002
Editorial: Physica-Verlag GmbH & Co
288 Páginas
Peso: 0,596 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2007-05-28T14:11:48-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-03-01T13:27:01-06:00 (Mexico City)
ISBN/EAN: 9783790815375

ISBN - escritura alterna:
3-7908-1537-3, 978-3-7908-1537-5
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: jin
Título del libro: advanced, applications soft computing, fuzzy systems, soft oft, studies design, fuzzy systeme, jin, design and applications

Datos del la editorial

Autor: Yaochu Jin
Título: Studies in Fuzziness and Soft Computing; Advanced Fuzzy Systems Design and Applications
Editorial: Physica; Physica
272 Páginas
Año de publicación: 2002-11-18
Heidelberg; DE
Idioma: Inglés
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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