
Nonlinear Dynamical Systems
Feedforward Neural Network Perspectives
Wiley (Publisher)
Published on 23. January 2001
Book
Hardback
XIV, 298 pages
978-0-471-34911-2 (ISBN)
Description
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.
Reviews / Votes
"...an interesting book, useful for researchers in network theory..." (Dynamical Systems Magazine, July 2006)More details
Product info
gebunden
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 21 mm
Weight
636 gr
ISBN-13
978-0-471-34911-2 (9780471349112)
Schweitzer Classification
Persons
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.
Content
Preface.
Feedforward Neural Networks: An Introduction (S. Haykin).
Uniform Approximation and Nonlinear Network Structures (I. Sandberg).
Robust Neural Networks (J. Lo).
Modeling, Segmentation, and Classification of Nonlinear Nonstationary Time Series (C. Fancourt & J. Principe).
Application of Feedforward Networks to Speech (S. Katagiri).
Index.