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Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises

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Abstract

Many control algorithms are based on the mathematical models of dynamic systems. System identification is used to determine the structures and parameters of dynamic systems. Some identification algorithms (e.g., the least squares algorithm) can be applied to estimate the parameters of linear regressive systems or linear-parameter systems with white noise disturbances. This paper derives two recursive extended least squares parameter estimation algorithms for Wiener nonlinear systems with moving average noises based on over-parameterization models. The simulation results indicate that the proposed algorithms are effective.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 51204149), the Fundamental Research Funds for the Central Universities (No. 2-9-2012-45), and the Open Project Fund of the Key Laboratory on Deep GeoDrilling Technology of the Ministry of Land and Resources (No. NLSD201213).

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  1. Key Laboratory on Deep GeoDrilling Technology of the Ministry of Land and Resources, China University of Geosciences, Beijing, 100083, PR China

    Yuanbiao Hu, Baolin Liu, Qin Zhou & Chun Yang

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  1. Yuanbiao Hu
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Correspondence to Yuanbiao Hu.

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Hu, Y., Liu, B., Zhou, Q. et al. Recursive Extended Least Squares Parameter Estimation for Wiener Nonlinear Systems with Moving Average Noises. Circuits Syst Signal Process 33, 655–664 (2014). https://doi.org/10.1007/s00034-013-9652-x

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  • DOI: https://doi.org/10.1007/s00034-013-9652-x

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