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Bias compensation-based parameter estimation for output error moving average systems. (English) Zbl 1263.93215

Summary: Identification problems of output error models with moving average noises are considered in this paper. The least-squares-based parameter estimation is biased under the colored noises in outputs. Firstly, a bias compensation term is formulated to achieve the bias-eliminated estimates of the system parameters. Secondly, the bias compensation term is determined by the unknown variance of the noise and the unknown noise model, thus based on the hierarchical identification principle, an unbiased parameter estimation is obtained by interactively estimating noise variance and noise parameters. Finally, the estimated bias compensation term is added to the biased parameter estimates. The simulation examples confirm the effectiveness of the proposed algorithm.

MSC:

93E10 Estimation and detection in stochastic control theory
93E12 Identification in stochastic control theory
93E24 Least squares and related methods for stochastic control systems
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