Deterministic learning theory for identification, control, and recognition by Cong Wang

Cover of: Deterministic learning theory for identification, control, and recognition | Cong Wang

Published by CRC Press in Boca Raton .

Written in English

Read online

Subjects:

  • Intelligent control systems,
  • Neural networks (Computer science),
  • Control theory

Edition Notes

Includes bibliographical references and index.

Book details

StatementCong Wang and David J. Hill.
SeriesAutomation and control engineering -- 29
ContributionsHill, David J.
Classifications
LC ClassificationsTJ217.5 .W355 2009
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL22527950M
ISBN 109780849375538
LC Control Number2008038057

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Deterministic Learning and Rapid Dynamical Pattern Recognition. based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of. He has authored and co-authored over 60 papers in international journals and conferences, and is a co-author of the book Deterministic Learning Theory for Identification, Recognition and Control (CRC Press, Boca Raton, FL, ).

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His research interests include dynamical pattern recognition, pattern-based intelligent control, oscillation fault diagnosis, and early detection of Author: Min Wang, Cong Wang. Recently, a deterministic learning theory was proposed for identification and rapid pattern recognition of uncertain nonlinear dynamical systems.

In this paper, we investigate deterministic learning of discrete-time nonlinear systems. For periodic or recurrent dynamical patterns, the persistent excitation (PE) condition can be satisfied by a regression subvector constructed from the neurons.

An Observer Approach for Deterministic Learning Using Patchy Neural Networks with Applications to Fuzzy Cognitive Networks: /ch In this paper, a new methodology is proposed for deterministic learning with neural networks. Using an observer that employs the integral of Cited by: 2. In order to reflect the actual content of the book, the present title was selected.

All the twenty-eight papers are roughly divided into two parts--Pattern Recognition and System Identification and Learning Process and Learning Control.

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This book introduces the concepts of deterministic learning theory and then discusses the persistent excitation property of RBF networks.

In this paper, we first address the uniformly exponential stability (UES) problem of a group of distributed cooperative adaptive systems in a general framework. Inspired by consensus theory, distributed cooperative adaptive laws are proposed to estimate the unknown parameters of these systems.

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The 2nd International Conference on Information Science and Engineering, Cited by: Deterministic learning theory for identification, recognition, and control. CRC Press. Cong Wang, David J. Hill. Year: Deterministic learning theory for identification, recognition, and control.

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