This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction.

The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation.

Key Features Of The Text

• The text and CD combine to become an interactive learning tool.
  
• Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations.
  
• Each key concept is followed by an interactive example.
  
• Over 200 fully functional simulations of adaptive systems are included.
  
• The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines.
  
• Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts.

The CD-ROM contains:

• A complete, electronic version of the text in hypertext format
  
• NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator
  
• A tutorial on how to use NeuroSolutions
  
• Additional data files to use with the simulator

Table of contents

Chapter 1: Data Fitting With Linear Models

Chapter 2: Pattern Recognition

Chapter 3: Multilayer Perceptions

Chapter 4: Designing and Training MLPS

Chapter 5: Function Approximation With MLPS, Radial Basis Functions, and Support Vector Machines

Chapter 6: Hebbian Learning and Principal Component Analysis

Chapter 7: Competitive and Kohonen NetwTrainingorks

Chapter 8: Principles of Digital Signal Processing

Chapter 9: Adaptive Filters

Chapter 10: Temporal Processing With Neural Networks

Chapter 11: Training and Using Recurrent Networks

Appendix A: Elements of Linear Algebra and pattern Recognition

Appendix B: Neurosolutions Tutorial

Appendix C: Data Directory

Glossary

Index