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Least squares estimation for a class of non-uniformly sampled systems based on the hierarchical identification principle

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Abstract

This paper presents a novel hierarchical least squares algorithm for a class of non-uniformly sampled systems. Based on the hierarchical identification principle, the identification model with a high dimensional parameter vector is decomposed into a group of submodels with lower dimensional parameter vectors. By using the least squares method to identify the submodels and taking a coordinated measure to address the associated items between the submodels, all the system parameters can be estimated. The proposed algorithm can save the computation cost. The performance analysis indicates that parameter estimates converge to their true values. The simulation tests confirm the convergence results.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (60973043, 61075008), the 111 Project (B12018), the University Graduate Innovation Research Program of Jiangsu Province (CX10D_229Z) and the Fundamental Research Funds for the Central Universities (JUDCF09029).

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Authors and Affiliations

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

    Yanjun Liu & Feng Ding

  2. Department of Mechanical Engineering, University of Victoria, Victoria, BC, V8N 3P6, Canada

    Yang Shi

Authors
  1. Yanjun Liu
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  2. Feng Ding
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  3. Yang Shi
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Correspondence to Yanjun Liu.

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Liu, Y., Ding, F. & Shi, Y. Least squares estimation for a class of non-uniformly sampled systems based on the hierarchical identification principle. Circuits Syst Signal Process 31, 1985–2000 (2012). https://doi.org/10.1007/s00034-012-9421-2

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  • DOI: https://doi.org/10.1007/s00034-012-9421-2

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