Coupled stochastic gradient algorithm and performance analysis for multivariable systems

被引：0
|
作者：
Liu Y.-J. [1 ,2 ]
Ding F. [1 ,2 ]
机构：
[1] Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi
[2] School of Internet of Things Engineering, Jiangnan University, Wuxi
来源：
Kongzhi yu Juece/Control and Decision | 2016年 / 31卷 / 08期
关键词：
Coupling identification concept; Multivariable systems; Parameter estimation; Performance analysis; Stochastic gradient;
D O I：
10.13195/j.kzyjc.2015.0571
中图分类号：
学科分类号：
摘要：
It is an issue that multivariable systems with high dimensions have many parameters, resulting in heavy computational costs in identification methods. Therefore, a coupled stochastic gradient algorithm is derived for multivariable systems based on the coupling identification concept. The identification model is decomposed into several single-output systems, and the parameter estimates are coupled during the subsystem identification by using the gradient search. The convergence properties are analyzed by using the martingale convergence theorem. Compared with the recursive least squares algorithm and the coupled least squares algorithm, the proposed algorithm has less computational load. The convergence rate can be improved by introducing a forgetting factor. Performance analysis verifies that the proposed algorithm converges. The simulation results show the effectiveness of the proposed algorithm. © 2016, Editorial Office of Control and Decision. All right reserved.
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页码：1487 / 1492
页数：5
相关论文
共 13 条
  • [1] El-Sherief H., Multivariable system structure and parameter identification using the correlation method, Automatica, 17, 3, pp. 541-544, (1981)
  • [2] Zheng W.X., On a least squares based algorithm for identification of stochastic linear systems, IEEE Trans on Signal Processing, 46, 6, pp. 1631-1638, (1998)
  • [3] Liu Y.J., Sheng J., Ding R.F., Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems, Computers & Mathematics with Applications, 59, 8, pp. 2615-2627, (2010)
  • [4] Jin Q.B., Dou J., Li S.N., Et al., Identification method based on accelerated convergence and hierarchical iterations for multivariable systems, J of Tsinghua University: Science and Technology, 52, 9, pp. 1194-1198, (2012)
  • [5] Fang C.Z., Xiao D.Y., Process Identification, pp. 424-430, (1988)
  • [6] Ding F., Chen T., Hierarchical gradient-based identification of multivariable discrete-time systems, Automatica, 41, 2, pp. 315-325, (2005)
  • [7] Ding F., System identification, Part H: Coupled identification concept and methods, J of Nanjing University of Information Science and Technology: Natural Science Edition, 4, 3, pp. 193-212, (2012)
  • [8] Ding F., Coupled-least-squares idnetification for multivariable systems, IET Control Theory and Applications, 7, 1, pp. 68-79, (2013)
  • [9] Liu Y.J., Ding F., Shi Y., A new parameter estimation algorithm for non-uniformly multirate sampled data systems, American Control Conf, pp. 84-89, (2011)
  • [10] Ding F., Liu G., Liu X., Partially coupled stochastic gradient identification methods for non-uniformly sampled systems, IEEE Trans on Automatic Control, 55, 8, pp. 1976-1981, (2010)
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