Neural Networks and Fuzzy Systems

1 Overview of Neural Networks.- 1.1 Brief History of Neural Network Research.- 1.2 Neural Network Models.- 1.3 Expectations for Neural Networks.- 2 The Hopfield Network.- 2.1 Definition of the Continuous Hopfield Network.- 2.2 Stability of Equilibrium Points.- 2.3 Suppression of Spurious States.- 2.4 Solution of the Hopfield Network.- 2.5 Variants of the Continuous Hopfield Network.- 2.6 Performance Evaluation for Traveling Salesman Problems and LSI Module Placement Problems.- Problems.- 3 Multilayered Networks.- 3.1 Network Training.- 3.2 Determination of the Network Structure.- 3.3 Synthesis of the Network.- 3.4 Pattern Classification by the Decision Tree Extracted from the Network.- 3.5 Acceleration of Training and Improvement of Generalization Ability.- Problems.- 4 Other Neural Networks.- 4.1 The Kohonen Network.- 4.2 Variants of Multilayered Networks.- 4.3 ART Models.- Problem.- 5 Overview of Fuzzy Systems.- 5.1 Fuzzy Sets.- 5.2 Fuzzy Rule Inference.- 5.3 Comparison of Neural Networks and Fuzzy Systems.- 5.4 Fuzzy Rule Extraction.- Problems.- 6 Fuzzy Rule Extraction for Pattern Classification from Numerical Data.- 6.1 Approximation by Cluster Centers.- 6.2 Approximation by Hyperboxes.- 6.3 Approximation by Polyhedrons.- 6.4 Performance Evaluation.- Problems.- 7 Fuzzy Rule Extraction for Function Approximation from Numerical Data.- 7.1 Clustering of Input Space.- 7.2 Clustering of Input and Output Spaces.- 7.3 Performance Evaluation of a Water Purification Plant and Time Series Prediction.- Problems.- 8 Composite Systems.- 8.1 Determining the Optimal Structure of the Composite Multilayered Network Classifier.- 8.2 Applications.- References.- Solutions to Problems.- Author Index.