Provides systematic design approaches for the identification, control, and recognition of nonlinear systems in uncertain environments. This book introduces the concepts of deterministic learning theory and then discusses the persistent excitation property of RBF networks. Series Editor(s): Lewis, Frank L. Series: Automation and Control Engineering. Num Pages: 207 pages, 147 black & white illustrations. BIC Classification: UYQE. Category: (P) Professional & Vocational. Dimension: 243 x 164 x 20. Weight in Grams: 508. . 2009. Hardback. . . . .

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Bibliographic Details

Title: Deterministic Learning Theory for Identification, Recognition, and Control
Publisher: Taylor & Francis Inc
Publication Date: 2009
Language: English
ISBN 10: 0849375533
ISBN 13: 9780849375538
Binding: Hardcover
Condition: New

About this title

Synopsis

Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation, and knowledge utilization in uncertain dynamic environments. It provides systematic design approaches for identification, recognition, and control of linear uncertain systems. Unlike many books currently available that focus on statistical principles, this book stresses learning through closed-loop neural control, effective representation and recognition of temporal patterns in a deterministic way.

A Deterministic View of Learning in Dynamic Environments

The authors begin with an introduction to the concepts of deterministic learning theory, followed by a discussion of the persistent excitation property of RBF networks. They describe the elements of deterministic learning, and address dynamical pattern recognition and pattern-based control processes. The results are applicable to areas such as detection and isolation of oscillation faults, ECG/EEG pattern recognition, robot learning and control, and security analysis and control of power systems.

A New Model of Information Processing

This book elucidates a learning theory which is developed using concepts and tools from the discipline of systems and control. Fundamental knowledge about system dynamics is obtained from dynamical processes, and is then utilized to achieve rapid recognition of dynamical patterns and pattern-based closed-loop control via the so-called internal and dynamical matching of system dynamics. This actually represents a new model of information processing, i.e. a model of dynamical parallel distributed processing (DPDP).

About the Author

Cong Wang, David J. Hill

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