This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to practical problems. Features Extensive coverage of training methods for both feedforward networks (including multilayer and radial basis networks) and recurrent networks. In addition to conjugate gradient and Levenberg-Marquardt variations of the backpropagation algorithm, the text also covers Bayesian regularization and early stopping, which ensure the generalization ability of trained networks. Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks. A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction, along with five chapters presenting detailed real-world case studies. Detailed examples and numerous solved problems. Slides and comprehensive demonstration software can be downloaded from hagan.okstate.edu/nnd.html.
Table of Contents
Chapter 1. Introduction
Chapter 2. Neuron Model and Network Architectures
Chapter 3. An Illustrative Example
Chapter 4. Perceptron Learning Rule
Chapter 5. Signal and Weight Vector Spaces
Chapter 6. Linear Transformations for Neural Networks
Chapter 7. Supervised Hebbian Learning
Chapter 8. Performance Surfaces and Optimum Points
Chapter 9. Performance Optimization
Chapter 10. Widrow-Hoff Learning
Chapter 11. Backpropagation
Chapter 12. Variations on Backpropagation
Chapter 13. Generalization
Chapter 14. Dynamic Networks
Chapter 15. Associative Learning
Chapter 16. Competitive Networks
Chapter 17. Radial Basis Networks
Chapter 18. Grossberg Network
Chapter 19. Adaptive Resonance Theory
Chapter 20. Stability
Chapter 21. Hopfield Network
Chapter 22. Practical Training Issues
Chapter 23. Case Study 1:Function Approximation
Chapter 24. Case Study 2:Probability Estimation
Chapter 25. Case Study 3:Pattern Recognition
Chapter 26. Case Study 4: Clustering
Chapter 27. Case Study 5: Prediction
