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Library,Documentation and Information Science Division

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Recursive identification and parameter estimation / Han-Fu Chen and Wenxiao Zhao.

By: Chen, Han-Fu.
Contributor(s): Zhao, Wenxiao [author].
Material type: TextTextPublisher: Boca Raton : CRC Press, c2014Description: xvii, 411 p. : illustrations ; 24 cm.ISBN: 9781466568846 (Hardback : acidfree paper).Subject(s): Systems engineering -- Mathematics | Parameter estimation | Recursive functionsDDC classification: 620.001171
Contents:
1. Dependent random vectors -- 2. Recursive parameter estimation -- 3. Recursive identification for ARMAX systems -- 4. Recursive identification for nonlinear systems -- 5. Other problems reducible to parameter estimation -- Appendices -- References -- Index.
Summary: This book describes the recursive approach to solving problems from system identification, parameter estimation, adaptive control problems, and more. Special attention is paid to how to transform the problems suitable to a recursive solution. Recursive solutions are given for identification of ARMAX systems without imposing restrictive conditions; for identification of typical nonlinear systems; for optimal adaptive tracking, iterative learning control of nonlinear systems, the consensus of multi-agents systems, and others; for some other related problems such as principal component analysis, the distributed randomized PageRank computation, and more.
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Includes bibliographical references and index.

1. Dependent random vectors --
2. Recursive parameter estimation --
3. Recursive identification for ARMAX systems --
4. Recursive identification for nonlinear systems --
5. Other problems reducible to parameter estimation --
Appendices --
References --
Index.

This book describes the recursive approach to solving problems from system identification, parameter estimation, adaptive control problems, and more. Special attention is paid to how to transform the problems suitable to a recursive solution. Recursive solutions are given for identification of ARMAX systems without imposing restrictive conditions; for identification of typical nonlinear systems; for optimal adaptive tracking, iterative learning control of nonlinear systems, the consensus of multi-agents systems, and others; for some other related problems such as principal component analysis, the distributed randomized PageRank computation, and more.

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