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Published Articles >> Table of Contents >> Abstract
International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06)
p. 42
Joint parameter and state estimation based on particle filtering and stochastic approximation
Xiaojun Yang, Xi’an Jiaotong University, Xi’an, China
Kunlin Shi, Xi’an Institute of Electromechanical Information Technology, Xi’an, China
Keyi Xing, Northwestern Polytechnical University, Xi’an, China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.138
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| Abstract |
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In this paper, an adaptive estimation algorithm is
proposed for non-linear dynamic systems with
unknown parameters based on combination of particle
filtering and SPSA technique. The estimates of
parameters are obtained by state samples and
maximum-likelihood estimation under particle
filtering, and the SPSA is used to approximate the
gradient of target function. The proposed algorithm
achieves joint estimation of dynamic state and static
parameters. Simulation result demonstrates the
efficiency of the algorithm.
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 Additional Information
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Citation:
Xiaojun Yang, Kunlin Shi, Keyi Xing,
"Joint parameter and state estimation based on particle filtering and stochastic approximation,"
cimca,
p. 42,
International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06),
2006
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