Neuro-Fuzzy Architectures and Hybrid Learning

• Κύριος συγγραφέας: Rutkowska, Danuta (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2002.
• Έκδοση: 1st ed. 2002.
• Σειρά: Studies in Fuzziness and Soft Computing, 85
• Θέματα: Artificial intelligence.; Artificial Intelligence.

 The main idea of this book is to present novel connectionist architectures of neuro-fuzzy systems, especially those based on the logical approach to fuzzy inference. In addition, hybrid learning methods are proposed to train the networks. The neuro-fuzzy architectures plus hybrid learning are considered as intelligent systems within the framework of computational and artificial intelligence. The book also provides an overview of fuzzy sets and systems, neural networks, learning algorithms (including genetic algorithms and clustering methods), as well as expert systems and perception-based systems which incorporates computing with words. 

 1 Introduction -- 2 Description of Fuzzy Inference Systems -- 2.1 Fuzzy Sets -- 2.2 Approximxate Reasoning -- 2.3 Fuzzy Systems -- 3 Neural Networks and Neuro-Fuzzy Systems -- 3.1 Neural Networks -- 3.2 Fuzzy Neural Networks -- 3.3 Fuzzy Inference Neural Networks -- 4 Neuro-Fuzzy Architectures Based on the Mamdani Approach -- 4.1 Basic Architectures -- 4.2 General Form of the Architectures -- 4.3 Systems with Inference Based on Bounded Product -- 4.4 Simplified Architectures -- 4.5 Architectures Based on Other Defuzzification Methods -- 4.6 Architectures of Systems with Non-Singleton Fuzzifier -- 5 Neuro-Fuzzy Architectures Based on the Logical Approach -- 5.1 Mathematical Descriptions of Implication-Based Systems -- 5.2 NOCFS Architectures -- 5.3 OCFS Architectures -- 5.4 Performance Analysis -- 5.5 Computer Simulations -- 6 Hybrid Learning Methods -- 6.1 Gradient Learning Algorithms -- 6.2 Genetic Algorithms -- 6.3 Clustering Algorithms -- 6.4 Hybrid Learning -- 6.5 Hybrid Learning Algorithms for Neuro-Fuzzy Systems -- 7 Intelligent Systems -- 7.1 Artificial and Computational Intelligence -- 7.2 Expert Systems -- 7.3 Intelligent Computational Systems -- 7.4 Perception-Based Intelligent Systems -- 8 Summary -- List of Figures -- List of Tables -- References.