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Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

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Abstract

This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61273194, 61203111), the Natural Science Foundation of Jiangsu Province (China, BK2012549) and the 111 Project (B12018).

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Authors and Affiliations

  1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, P.R. China

    Peipei Hu

  2. Control Science and Engineering Research Center, Jiangnan University, Wuxi, 214122, P.R. China

    Feng Ding

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  1. Peipei Hu
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  2. Feng Ding
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Correspondence to Feng Ding.

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Hu, P., Ding, F. Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle. Nonlinear Dyn 73, 583–592 (2013). https://doi.org/10.1007/s11071-013-0812-0

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