Neural Network Design (2nd Edition), by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. This book gives an introduction to basic neural network architectures and learning rules. Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their application to practical engineering problems in such areas as nonlinear regression, pattern recognition, signal processing, data mining and control systems.
Topics included: Introduction • Neuron Model and Network Architectures • An Illustrative Example • Perceptron Learning Rule • Signal and Weight Vector Spaces • Linear Transformations for Neural Networks • Supervised Hebbian Learning • Performance Surfaces and Optimum Points • Performance Optimization • Widrow-Hoff Learning • Backpropagation • Variations on Backpropagation • Generalization • Dynamic Networks • Associative Learning • Competitive Networks • Radial Basis Networks • Grossberg Network • Adaptive Resonance Theory • Stability • Hopfield Network • Practical Training Issues • Case Study 1:Function Approximation • Case Study 2:Probability Estimation • Case Study 3:Pattern Recognition • Case Study 4: Clustering • Case Study 5: Prediction.
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Publisher: Martin Hagan
Published: September, 2014
File size: – 11.27 MB (PDF)
Number of pages: – 1012
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