Decision theoretic approach to real-time robust identification
作者:
BRANKOD. KOVAČEVIĆ,
期刊:
International Journal of Systems Science
(Taylor Available online 1990)
卷期:
Volume 21,
issue 12
页码: 2393-2407
ISSN:0020-7721
年代: 1990
DOI:10.1080/00207729008910560
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
A decision-theoretic approach to the estimation of unknown parameters from a linear discrete-time dynamic measurement model in the presence of disturbance uncertainty is considered. The unknown disturbance statistics are characterized by a certain class of distributions to which the real disturbance distribution is confined. Using game theory and the asymptotic estimation error covariance matrix as the criteria of how good an estimator is, the stochastic gradient-type algorithm is shown to be optimal in the min-max sense. Since the optimal solution is not tractable in practice, several suboptimal procedures are derived on the basis of suitable approximations. The convergence of the derived algorithms is established theoretically using the ordinary differential equation approach. Monte Carlo simulation results are presented for the quantitative performance evaluation of the algorithms.
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