Xiaoming Chen

Jiangnan University, Wu-hsi, Jiangsu Sheng, China

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Publications (3)1.41 Total impact

  • Source
    Jie Ding · Lili Han · Xiaoming Chen
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    ABSTRACT: This paper focuses on parameter estimation problems of auto-regression (AR) time series models with missing observations. The standard estimation algorithms cannot be applied to such AR models with missing observations. The polynomial transformation technique is employed to transform the AR models into models which can be identified from available scarce observations, then the extended stochastic gradient algorithm is proposed to fit the time series with missing observations. The convergence properties of the proposed algorithm are analyzed and an example is given to test and illustrate the conclusions in the paper.
    Preview · Article · Mar 2010 · Mathematical and Computer Modelling
  • Source
    Yuwu Liao · Dongqing Wang · Xiaoming Chen · Feng Ding
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    ABSTRACT: This paper uses the polynomial transformation technique to transform an ARX model into a special model that can be identified with dual-rate input-output data, and presents the residual based stochastic gradient algorithm for dual-rate sampled-data systems, and studies convergence properties of the algorithm involved. The analysis indicates that the parameter estimation error consistently converges to zero under some proper conditions. Finally, we test the algorithms proposed in paper by a simulation example and show their effectiveness.
    Preview · Conference Paper · Jul 2009
  • Jie Ding · Xiaoming Chen · Feng Ding
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    ABSTRACT: This paper focuses on identification problems of auto-regression (AR) models with missing output observation data. The standard least squares algorithm cannot be applied to the AR models due to the missing output data. To estimate the parameters of the AR models, we employ the polynomial transformation technique to transform the AR models into the auto-regression moving average (ARMA) models, which can be identified from available scarce observation data. Then, we analyze the convergence properties of the algorithm in details and give an example to test and illustrate the algorithm involved.
    No preview · Conference Paper · Jun 2008

Publication Stats

73 Citations
1.41 Total Impact Points

Institutions

  • 2008-2010
    • Jiangnan University
      • School of Communication and Control Engineering
      Wu-hsi, Jiangsu Sheng, China