Neural Smithing : Supervised Learning in Feedforward Artificial Neural Networks

Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research. Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptions (MLP). These are the most widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research. Contents Preface 1 Introduction 2 Supervised Learning 3 Single-Layer Networks 4 MLP Representational Capabilities 5 Back-Propagation 6 Learning Rate and Momentum 7 Weight-Initialization Techniques 8 The Error Surface 9 Faster Variations of Back-Propagation 10 Classical Optimization Techniques 11 Genetic Algorithms and Neural Networks 12 Constructive Methods 13 Pruning Algorithms 14 Factors Influencing Generalization 15 Generalization Prediction and Assessment 16 Heuristics for Improving Generalization 17 Effects of Training with Noisy Inputs A Linear Regression B Principal Components Analysis C Jitter Calculations D Sigmoid-like Nonlinear Functions References Index Russell D. Reed And Robert J. Marks Ii. A Bradford Book. Includes Bibliographical References (p. [319]-338) And Index.