Identification of hammerstein-wiener models based on bias compensation recursive least squares

被引：8
|
作者：
Li Y. [1 ]
Mao Z.-Z. [1 ]
Wang Y. [2 ]
Yuan P. [1 ]
Jia M.-X. [1 ]
机构：
[1] School of Information Science and Engineering, Northeastern University
[2] Liaoyang Municipal Development and Reform Commission
来源：
Zidonghua Xuebao/ Acta Automatica Sinica | 2010年 / 36卷 / 01期
关键词：
Bias compensation recursive least squares (BCRLS); Hammerstein-Wiener systems; Singular value decomposition;
D O I：
10.3724/SP.J.1004.2010.00163
中图分类号：
学科分类号：
摘要：
Many actual systems can be represented by the Hammerstein-Wiener model, where a linear dynamic system is surrounded by two static nonlinearities at its input and output. An improved on-line two stage identification algorithm is pro-posed to identify the Hammerstein-Wiener model with process noise. Firstly, the bias compensation recursive least squares is adopted to identify the parameter vector containing the product of the original system parameters. The estimation bias is compensated by introducing a correction term in the recursive least squares estimate. Secondly, the singular value decomposition method based on the tensor product approach is adopted to separate each parameter value from the original system. The accuracy of parameter separation is improved by introducing the tensor product of two matrixes to approach the weight coefficient of the weighted least squares. Theoretical analysis and computer simulation validate the effectiveness of the proposed algorithm. © 2010 Acta Automatica Sinica. All rights reserved.
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页码：163 / 168
页数：5
相关论文
共 18 条
  • [1] Tan A.H., Godfrey K., Identification of Wiener-Hammerstein models using linear interpolation in the frequency domain (LIFRED), IEEE Transactions on Instrumentation and Measurement, 51, 3, pp. 509-521, (2002)
  • [2] Liu Y., Bai E.W., Iterative identification of Hammerstein systems, Automatica, 43, 2, pp. 346-354, (2007)
  • [3] Yuan T.-Q., Liu W.-J., Identification of Hammerstein model based on dynamical separation technology, Control Theory and Applications, 19, 4, pp. 619-622, (2002)
  • [4] Xiang W., Chen Z.-H., New identification method of nonlinear systems based on Hammerstein models, Control Theory and Applications, 24, 1, pp. 143-147, (2007)
  • [5] Ding F., Chen T.W., Identification of Hammerstein nonlinear ARMAX systems, Automatica, 41, 9, pp. 1479-1489, (2005)
  • [6] Ding F., Shi Y., Chen T.W., Gradient-based identification methods for Hammerstein nonlinear ARMAX models, Nonlinear Dynamics, 45, 1-2, pp. 31-43, (2006)
  • [7] Ding F., Shi Y., Chen T.W., Auxiliary model based least-squares identification methods for Hammerstein output-error systems, Systems and Control Letters, 56, 5, pp. 373-380, (2007)
  • [8] Ding F., Chen T.W., Combined parameter and output estimation of dual-rate systems using an auxiliary model, Automatica, 40, 10, pp. 1739-1748, (2004)
  • [9] Ding F., Chen T.W., Identification of dual-rate systems based on finite impulse response models, International Journal of Adaptive Control and Signal Processing, 18, 7, pp. 589-598, (2004)
  • [10] Bai E.W., An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems, Automatica, 34, 3, pp. 333-338, (1998)
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