Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives

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John Wiley & Sons, Feb 21, 2001 - Computers - 298 pages
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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.

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An Introduction
Uniform Approximation
Robust Neural Networks
Modeling Segmentation and Classification
Application of Feedforward Networks to Speech

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Page v - Our main result is a theorem that gives, in a certain setting, a necessary and sufficient condition under which discrete-space multidimensional shift-invariant input-output maps with vector-valued inputs drawn from a certain large set can be uniformly approximated arbitrarily well, using a structure consisting of a linear preprocessing stage followed by a memoryless nonlinear network.

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About the author (2001)

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.