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ISBN
:
9788131503959
Publisher
:
Nelson Candad
Subject
:
Encyclopaedias & Reference Works
Binding
:
Paperback
Pages
:
736
Year
:
2008
₹
625.0
₹
518.0
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"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 pattern recognition signal processing and control system" Key Feature This Book has been Organized for A one-semester Introductory Course In Neural Networks At The Senior Or First Year Graduate Level Each Chapter ?Material Is Presented In Clear And Consistent Manner To Facilitate Readability And Applicability With Ease. * Inclusion Of Many Solved Problems To Illustrate Each Topic Of Discussion Various Introductory Materials And Chapters On Applied Mathematics Are Included Table of Contents Preface 1. Introduction 2. Neuron Model and Network Architectures 3. An Illustrative Example 4. Perceptron Learning Rule 5. Signal and Weight Vector Spaces 6. Linear Transformations for Neural Networks 7. Supervised Hebbian Learning 8. Performance Surfaces and Optimum Points 9. Performance Optimization 10. Widrow-Hoff Learning 11. Back propagation 12. Variations on Backpropagation 13. Associative Learning 14. Competitive Networks 15. Grossberg Network 16. Adaptive Resonance Theory 17. Stability 18. Hopfield Network 19. Epilogue
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