"Handbook of Neural Computation" by Emile Fiesler, Russell Beale

This book is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems.The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar problems.

This handbook has been designed to provide valuable information to a diverse readership. Through regular supplements, the handbook will remain fully up to date and will develop and evolve along with the research field that it represents.

Contents
Preface
Foreword
How to Use This Handbook
PART A. INTRODUCTION
A1 Neural Computation: The Background
A2 Why Neural Networks?
PART B. FUNDAMENTAL CONCEPTS OF NEURAL COMPUTATION
B1 The Artificial Neuron
B2 Neural Network Topologies
B3 Neural Network Training
B4 Data Input and Output Representations
B5 Network Analysis Techniques
B6 Neural Networks: A Pattern Recognition Perspective
PART C. PART C NEURAL NETWORK MODELS
C1 Supervised Models
C2 Unsupervised Models
C3 Reinforcement Learning
PART D. HYBRID APPROACHES
D1 Neuro-fuzzy systems
D2 Neural-Evolutionary Systems
PART E. NEURAL NETWORK IMPLEMENTATIONS
E1 Neural Network Hardware Implementations
PART F. APPLICATIONS OF NEURAL COMPUTATION
F1 Neural Network Applications
PART G. NEURAL NETWORKS IN PRACTICE: CASE STUDIES
G1 Perception and Cognition
G2 Engineering
G3 Physical Sciences
G4 Biology and Biochemistry
G5 Medicine
G6 Economics, Finance and Business
G7 Computer Science
G8 Arts and Humanities
PART H. THE NEURAL NETWORK RESEARCH COMMUNITY
H1 Future Research in Neural Computation
List of Contributors
Index