Neuro-Computers
Optimization Based Learning
Published on 29. January 2003
Book
Hardback
142 pages
978-0-8493-1713-2 (ISBN)
Description
The brain-like architecture of artificial neural networks makes them ideal for tackling problems that are too difficult for conventional architectures, specifically problems that involve pattern recognition or other perceptual tasks.
Neuro-Computers: Optimization Based Learning provides an intermediate-level exposition of the exciting world of neuro-computers. It presents the importance of neuro-computing to artificial intelligence, giving historical background and present-day implementation options. The book demonstrates the superiority of the adaptive search strategy over conventional fixed parameter searches performed by backpropagation algorithms. It then explores global optimization strategy and presents genetic algorithms as viable methods to train neuro computers on non-trivial problems.
This self-contained volume is delivered in a format that is suitable for graduate students, as well as researchers who want to begin work in neuro-computing or related artificial intelligence applications.
Neuro-Computers: Optimization Based Learning provides an intermediate-level exposition of the exciting world of neuro-computers. It presents the importance of neuro-computing to artificial intelligence, giving historical background and present-day implementation options. The book demonstrates the superiority of the adaptive search strategy over conventional fixed parameter searches performed by backpropagation algorithms. It then explores global optimization strategy and presents genetic algorithms as viable methods to train neuro computers on non-trivial problems.
This self-contained volume is delivered in a format that is suitable for graduate students, as well as researchers who want to begin work in neuro-computing or related artificial intelligence applications.
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
Weight
499 gr
ISBN-13
978-0-8493-1713-2 (9780849317132)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Person
Content
Foreword
Preface
Notations and Symbols
Introduction
Neurocomputing
Organization of the Brain
The Neuron Model
Importance of the Connectionist Approach
Motivation
Historical Background
The Adaptive Linear Combiner
Artificial Neural Network Models
Learning Algorithms for Feedforward Networks
Implementation
Applications
Composite Optimization Strategy
Pattern Processing for Intelligent Behavior
The Power of Hidden Units
Supervised Learning
The Optimization Approach
Steepest Descent
Optimal Step Length
Adaptation Heuristics
The Search for Global Solution
Multimodal Performance Surfaces
Simulated Annealing
The Method of Covering
Implementation of the Method of Covering
Generalization and Fault Tolerance
ANN Performance Issues
Better Performance Metrices
Constrained Optimization for Better Generalization
Fault Tolerance Constraints
Development of Fault Tolerant ANNs
Genetic Training in Neuro-Computers
Global-Minimization for Feedforward Neural Networks
Genetic Algorithm: A Global Optimizer
Genetic Learning in Neural Networks
Neuro-Computer Training Based on Advanced Genetic Operators
Two-Parents Multipoint Restricted Crossover (Double-MRX)
Three-Parents Multipoint Restricted Crossover (Triple-MRX)
Elitist Selection
Mutation Scheduling
Hybrid Learning
Simulation and Case Studies
The Simulation System
Learning Binary Mapping
Fault Tolerance and Generalization
Performance of Genetically Trained Neural Network
Performance of Hybrid Learning
Epilogue
Bibliography
Index
Preface
Notations and Symbols
Introduction
Neurocomputing
Organization of the Brain
The Neuron Model
Importance of the Connectionist Approach
Motivation
Historical Background
The Adaptive Linear Combiner
Artificial Neural Network Models
Learning Algorithms for Feedforward Networks
Implementation
Applications
Composite Optimization Strategy
Pattern Processing for Intelligent Behavior
The Power of Hidden Units
Supervised Learning
The Optimization Approach
Steepest Descent
Optimal Step Length
Adaptation Heuristics
The Search for Global Solution
Multimodal Performance Surfaces
Simulated Annealing
The Method of Covering
Implementation of the Method of Covering
Generalization and Fault Tolerance
ANN Performance Issues
Better Performance Metrices
Constrained Optimization for Better Generalization
Fault Tolerance Constraints
Development of Fault Tolerant ANNs
Genetic Training in Neuro-Computers
Global-Minimization for Feedforward Neural Networks
Genetic Algorithm: A Global Optimizer
Genetic Learning in Neural Networks
Neuro-Computer Training Based on Advanced Genetic Operators
Two-Parents Multipoint Restricted Crossover (Double-MRX)
Three-Parents Multipoint Restricted Crossover (Triple-MRX)
Elitist Selection
Mutation Scheduling
Hybrid Learning
Simulation and Case Studies
The Simulation System
Learning Binary Mapping
Fault Tolerance and Generalization
Performance of Genetically Trained Neural Network
Performance of Hybrid Learning
Epilogue
Bibliography
Index