Foreword; Anca Ralescu. Preface. Introduction. 1. Overview of Neural Networks. 2. The Hopfield Network. 3. Multilayered Networks. 4. Other Neural Networks. 5. Overview of Fuzzy Systems. 6. Fuzzy Rule Extraction for Pattern Classification from Numerical Data. 7. Fuzzy Rule Extraction for Function Approximation from Numerical Data. 8. Composite Systems. References. Solutions to Problems. Index: Subject Index. Author Index.
Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial optimization problems, multilayered neural networks for pattern classification and function approximation, fuzzy systems that have the same functions as multilayered networks, and composite systems that have been successfully applied to real world problems. The author also includes representative neural network models such as the Kohonen network and radial basis function network. New fuzzy systems with learning capabilities are also covered.
The advantages and disadvantages of neural networks and fuzzy systems are examined. The performance of these two systems in license plate recognition, a water purification plant, blood cell classification, and other real world problems is compared.
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