Pattern Classification : Neuro-fuzzy Methods and Their Comparison

Neural networks have a learning capability but analysis of a trained network is difficult. On the other hand, extraction of fuzzy rules is difficult but once they have been extracted, it is relatively easy to analyze the fuzzy system. This book solves the above problems by developing new learning paradigms and architectures for neural networks and fuzzy systems. The book consists of two parts: Pattern Classification and Function Approximation. In the first part, based on the synthesis principle of the neural-network classifier: A new learning paradigm is discussed and classification performance and training time of the new paradigm for several real-world data sets are compared with those of the widely-used back-propagation algorithm; Fuzzy classifiers of different architectures based on fuzzy rules can be defined with hyperbox, polyhedral, or ellipsoidal regions. The book discusses the unified approach for training these fuzzy classifiers; The performance of the newly-developed fuzzy classifiers and the conventional classifiers such as nearest-neighbor classifiers and support vector machines are evaluated using several real-world data sets and their advantages and disadvantages are clarified. In the second part: Function approximation is discussed extending the discussions in the first part; Performance of the function approximators is compared. This book is aimed primarily at researchers and practitioners in the field of artificial intelligence and neural networks. Front Matter....Pages I-XIX Front Matter....Pages 1-1 Introduction....Pages 3-20 Multilayer Neural Network Classifiers....Pages 21-46 Support Vector Machines....Pages 47-61 Membership Functions....Pages 63-80 Static Fuzzy Rule Generation....Pages 81-107 Clustering....Pages 109-118 Tuning of Membership Functions....Pages 119-157 Robust Pattern Classification....Pages 159-175 Dynamic Fuzzy Rule Generation....Pages 177-196 Comparison of Classifier Performance....Pages 197-204 Optimizing Features....Pages 205-237 Generation of Training and Test Data Sets....Pages 239-247 Front Matter....Pages 249-249 Introduction....Pages 251-255 Fuzzy Rule Representation and Inference....Pages 257-261 Fuzzy Rule Generation....Pages 263-286 Robust Function Approximation....Pages 287-297 Back Matter....Pages 299-327

This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.