Neural Networks and Fuzzy Systems: Theory and Applications

✍ Scribed by Shigeo Abe (auth.)

Publisher: Springer US
Year: 1997
Tongue: English
Leaves: 265
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xvi
Overview of Neural Networks....Pages 1-5
The Hopfield Network....Pages 7-43
Multilayered Networks....Pages 45-91
Other Neural Networks....Pages 93-125
Overview of Fuzzy Systems....Pages 127-149
Fuzzy Rule Extraction for Pattern Classification from Numerical Data....Pages 151-197
Fuzzy Rule Extraction for Function Approximation from Numerical Data....Pages 199-208
Composite Systems....Pages 209-224
Back Matter....Pages 225-258

✦ Subjects

Artificial Intelligence (incl. Robotics);Statistical Physics, Dynamical Systems and Complexity;Mathematical Logic and Foundations

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