Artificial Neural Networks

are a computational tool, based on the properties of biological neural systems. Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.

Table of Contents

 * Introduction

Overview

 * Neural Network Basics
 * Biological Neural Networks
 * History
 * MATLAB Neural Networking Toolbox
 * Activation Functions

ANN Models

 * Feed-Forward Networks
 * Radial Basis Function Networks
 * Recurrent Networks
 * Echo State Networks
 * Hopfield Networks
 * Self-Organizing Maps
 * Competitive Models
 * ART Models
 * Boltzmann Machines
 * Committee of Machines
 * Autoencoders
 * Convolutional Neural Networks

Teaching and Learning

 * Learning Paradigms
 * Error-Correction Learning
 * Hebbian Learning
 * Competitive Learning
 * Boltzmann Learning
 * ART Learning
 * Self-Organizing Maps

Applications

 * Pattern Recognition
 * Clustering
 * Feature Detection
 * Series Prediction
 * Data Compression
 * Curve Fitting
 * Optimization
 * Control

Future Work

 * Criticisms and Problems
 * Artificial Intelligence

Resources

 * Resources
 * Licensing