Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives

Irwin W. Sandberg/ James T. Lo/ Craig L. Fancourt/ José C. Principe/ Shigeru Katagiri/ Simon Haykin

ISBN 10: 0471349119 / ISBN 13: 9780471349112

Published by Wiley-Interscience, 2001

About this Item

1st edition. 312 pages. 9.50x6.50x0.75 inches. In Stock.

Seller Inventory # x-0471349119

Bibliographic Details

• Title: Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives
• Publisher: Wiley-Interscience
• Publication Date: 2001
• Language: English
• ISBN 10: 0471349119
• ISBN 13: 9780471349112
• Binding: Hardcover
• Condition: Brand New

About this title

Synopsis

The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural network structures
Considered one of the most important types of structures in the study of neural networks and neural-like networks, feedforward networks incorporating dynamical elements have important properties and are of use in many applications. Specializing in experiential knowledge, a neural network stores and expands its knowledge base via strikingly human routes-through a learning process and information storage involving interconnection strengths known as synaptic weights.
In Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives, six leading authorities describe recent contributions to the development of an analytical basis for the understanding and use of nonlinear dynamical systems of the feedforward type, especially in the areas of control, signal processing, and time series analysis. Moving from an introductory discussion of the different aspects of feedforward neural networks, the book then addresses:
* Classification problems and the related problem of approximating dynamic nonlinear input-output maps
* The development of robust controllers and filters
* The capability of neural networks to approximate functions and dynamic systems with respect to risk-sensitive error
* Segmenting a time series
It then sheds light on the application of feedforward neural networks to speech processing, summarizing speech-related techniques, and reviewing feedforward neural networks from the viewpoint of fundamental design issues. An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries.

About the Author

IRWIN W. SANDBERG is a chaired professor at the University of Texas at Austin.
JAMES T. LO teaches in the Department of Mathematics and Statistics, University of Maryland.
CRAIG L. FANCOURT is a member of the Adaptive Image and Signal Processing Group at the Sarnoff Corp. in Princeton, New Jersey.
JOSE C. PRINCIPE is BellSouth Professor in the Electrical and Computer Engineering Department at the University of Florida, Gainesville.
SHIGERU KATAGIRI leads research on speech and hearing at NTT Communication Science Laboratories, Kyoto, Japan.
SIMON HAYKIN teaches at McMaster University in Hamilton, Ontario, Canada. He has authored or coauthored over a dozen Wiley titles.