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

By: Contributor(s): Material type: TextTextPublication details: Boca Raton : CRC Press, c2014.Description: xvii, 411 p. : illustrations ; 24 cmISBN:
  • 9781466568846 (Hardback : acidfree paper)
Subject(s): DDC classification:
  • 620.001171 23 C518
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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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 620.001171 C518 (Browse shelf(Opens below)) Available 136655
Total holds: 0

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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