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Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory

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Abstract

This paper is concerned with the identification problem for multivariable equation-error systems with autoregressive moving average noise using the hierarchical identification principle and the multi-innovation identification theory. We propose a hierarchical gradient-based iterative (HGI) identification algorithm and give a gradient-based iterative (GI) identification algorithm for comparison. Meanwhile, the multi-innovation theory is used to derive the hierarchical multi-innovation gradient-based iterative (HMIGI) identification algorithm. The analysis shows that the HGI algorithm has smaller computational burden and can give more accurate parameter estimates than the GI algorithm and the HMIGI algorithm can track time-varying parameters. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61873111, 61803049), the Natural Science Foundation of Shandong Province (ZR201702170236) and the Natural Science Fundamental Research Project of Colleges and Universities in Jiangsu Province (No. 17KJB120001).

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Authors and Affiliations

  1. College of Automation and Electronic Engineering, Institute of Artificial Intelligence and Control, Qingdao University of Science and Technology, Qingdao, 266061, People’s Republic of China

    Lijuan Wan & Feng Ding

  2. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, People’s Republic of China

    Feng Ding

  3. Internet of Things Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Feng Ding

  4. Department of Mathematics, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    Feng Ding

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  1. Lijuan Wan
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Correspondence to Lijuan Wan or Feng Ding.

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Wan, L., Ding, F. Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory. Circuits Syst Signal Process 38, 2971–2991 (2019). https://doi.org/10.1007/s00034-018-1014-2

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