Neural and Adaptive Systems

Fundamentals Through Simulations

J.C. Principe, W.Curt Lefebvre

656 pages, parution le 10/11/1999

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Résumé

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

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	J.C. Principe, W.Curt Lefebvre
Parution	10/11/1999
Nb. de pages	656
Format	19,4 x 24
Couverture	Relié
Poids	1250g
EAN13	9780471351672
ISBN13	978-0-471-35167-2