Nerual Network Design (2nd Edition)
Content
Ch 2 Neuron Model and Network Architectures
Ch 3 An Illustrative Example
Ch 4 Perceptron Learning Rule
Ch 5 Signal and Weight Vector Spaces
Ch 6 Linear Transformations for Neural Networks
Ch 7 Supervised Hebbian Learning
Ch 8 Performance Surfaces and Optimum Points
Ch 9 Performance Optimization
Ch 10 Widrow-Hoff Learning
Ch 11 Backpropagation
Ch 12 Variations on Backpropagation
Ch 13 Generalization
Ch 14 Dynamic Networks
Ch 15 Associative Learning
Ch 16 Competitive Networks
Ch 17 Radial Basis Networks
Ch 18 Grossberg Network
Ch 19 Adaptive Resonance Theory
Ch 20 Stability
Ch 21 Hopfield Network
Ch 22 Practical Training Issues
Ch 23 Case Study 1:Function Approximation
Ch 24 Case Study 2:Probability Estimation
Ch 25 Case Study 3:Pattern Recognition
Ch 26 Case Study 4: Clustering
Ch 27 Case Study 5: Prediction
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