Neural and Adaptive Systems

Like no other text in this field, authors Jose C. Principe, Neil R. Euliano, and W. Curt Lefebvre have written a unique and innovative text unifying the concepts of neural networks and adaptive filters into a common framework.

The text is suitable for senior/graduate courses in neural networks and adaptive filters. It offers over 200 fully functional simulations (with instructions) to demonstrate and reinforce key concepts and help the reader develop an intuition about the behavior of adaptive systems with real data. This creates a powerful self-learning environment highly suitable for the professional audience.

Ein einzigartiger und innovativer Ansatz zur Vermittlung von Kenntnissen über adaptive und neuronale Netze! Der Leser experimentiert mit realen Datensätzen und lernt so, das Verhalten der Netzwerke intuitiv einzuschätzen. Als interaktives Lehr- und Lernmittel bietet das Buch über 200 voll funktionsfähige Simulationen mit ausführlichen Arbeitsanleitungen - ideal auch zum Selbststudium! Die elektronische Version des Buches enthält neben dem vollständigen Text interessante Extras. (01/00)

Chapter 1 Data Fitting with Linear Models 1

Chapter 2 Pattern Recognition 68

Chapter 3 Multilayer Perceptrons 100

Chapter 4 Designing and Training MLPS 173

Chapter 5 Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines 223

Chapter 6 Hebbian Learning and Principal Component Analysis 279

Chapter 7 Competitive and Kohonen Networks 333

Chapter 8 Principles of Digital Signal Processing 364

Chapter 9 Adaptive Filters 429

Chapter 10 Temporal Processing with Neural Networks 473

Chapter 11 Training and Using Recurrent Networks 525

Appendix A Elements of Linear Algebra and Pattern Recognition 589

Appendix B NeuroSolutions Tutorial 613

Appendix C Data Directory 637

Glossary 639

Index 647

Jose C. Principe, University of Florida.

Neil R. Euliano, NeuroDimension, Inc.

W. Curt Lefebvre, NeuroDimension, Inc.

Develop New Insight into the Behavior of Adaptive Systems 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

"An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight."
—James Zeidler, University of California, San Diego