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The Gradient-Based Iterative Estimation Algorithms for Bilinear Systems with Autoregressive Noise

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Abstract

This paper considers the parameter identification problem of bilinear systems, which are a special class of nonlinear systems. The basic idea is giving the input–output representation of the bilinear system through eliminating the state variables in the system. By using the hierarchical identification principle and the data filtering technique, we derive a gradient-based iterative (GI) algorithm, a hierarchical GI algorithm and a filtering-based GI algorithm for identifying the parameters of bilinear systems with colored noises. The simulation results indicate that the proposed algorithms are effective for identifying bilinear systems.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61472195) and the Taishan Scholar Project Fund of Shandong Province of China.

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Authors and Affiliations

  1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266042, People’s Republic of China

    Meihang Li, Ximei Liu & Feng Ding

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  1. Meihang Li
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  2. Ximei Liu
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  3. Feng Ding
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Correspondence to Ximei Liu.

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Li, M., Liu, X. & Ding, F. The Gradient-Based Iterative Estimation Algorithms for Bilinear Systems with Autoregressive Noise. Circuits Syst Signal Process 36, 4541–4568 (2017). https://doi.org/10.1007/s00034-017-0527-4

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  • DOI: https://doi.org/10.1007/s00034-017-0527-4

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