by Anthony Zaknich (The University of Western Australia, Australia)

This book provides a thorough theoretical and practical introduction to the application of neural networks to pattern recognition and intelligent signal processing. It has been tested on students, unfamiliar with neural networks, who were able to pick up enough details to successfully complete their masters or final year undergraduate projects. The text also presents a comprehensive treatment of a class of neural networks called common bandwidth spherical basis function NNs, including the probabilistic NN, the modified probabilistic NN and the general regression NN.

• A Brief Historical Overview
• Basic Concepts
• ANN Performance Evaluation
• Basic Pattern Recognition Principles
• ADALINES, Adaptive Filters, and Multi-Layer Perceptrons
• Probabilistic Neural Network Classifier
• General Regression Neural Network
• The Modified Probabilistic Neural Network
• Advanced MPNN Developments
• Neural Networks Similar to the Common Bandwidth Spherical Basis Function Regression ANNs
• Unsupervised Learning Neural Networks
• Other Neural Network Models
• Statistical Learning Theory
• Application to Intelligent Signal Processing
• Application to Intelligent Control