Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems

Guomin ZHONG; Mingxuan SUN

doi:10.11999/JEIT200882

Volume 43 Issue 9

Sep. 2021

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Guomin ZHONG, Mingxuan SUN. Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2594-2600. doi: 10.11999/JEIT200882

Citation:

Guomin ZHONG, Mingxuan SUN. Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2594-2600. doi: 10.11999/JEIT200882

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Iterative Learning Identification for a Class of Wiener Nonlinear Time- Varying Systems

doi: 10.11999/JEIT200882 cstr: 32379.14.JEIT200882

Guomin ZHONG^,,
Mingxuan SUN

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds: The National Natural Science Foundation of China(62073291)

Received Date: 2020-10-16
Rev Recd Date: 2021-03-11

Available Online: 2021-04-15

Publish Date: 2021-09-16

Abstract

Abstract

For the parameters identification of Wiener nonlinear time-varying systems, iterative learning algorithms based on repeated axes are proposed to estimate the time-varying or even abrupt parameters. At first, the output nonlinear part of the Wiener system undertaken is tackled based on polynomial expansion, and then the regression model is constructed, the unknown parameters and intermediate variables are replaced by their estimates. Both iterative learning gradient and iterative learning least square algorithms are used to conduct the identification of the time-varying systems. Compared with the recursive algorithm with forgetting factor and iterative learning gradient algorithm, the simulation results demonstrate that the iterative learning least squares algorithm can perform high identification accuracy and efficiency, being of fast convergence speed and less resultant system output error, which verifies the effectiveness of the proposed algorithm.
- Time-varying parameters,
- Wiener systems,
- Gradient algorithms,
- Least squares,
- Iterative learning

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References(21)

References

[1]	丁锋. 系统辨识新论[M]. 北京: 科学出版社, 2013: 37–38.
[2]	GIRI F and BAI Erwei. Block-oriented Nonlinear System Identification[M]. London: Springer, 2010: 3–11. doi: 10.1007/978-1-84996-513-2_1.
[3]	DING Feng, LIU Ximei, and LIU Manman. The recursive least squares identification algorithm for a class of wiener nonlinear systems[J]. Journal of the Franklin Institute, 2016, 353(7): 1518–1526. doi: 10.1016/j.jfranklin.2016.02.013
[4]	JIN Xing, HUANG Biao, and SHOOK D S. Multiple model LPV approach to nonlinear process identification with EM algorithm[J]. Journal of Process Control, 2011, 21(1): 182–193. doi: 10.1016/j.jprocont.2010.11.008
[5]	ZHOU Lincheng, LI Xiangli, and PAN Feng. Gradient-based iterative identification for MISO Wiener nonlinear systems: Application to a glutamate fermentation process[J]. Applied Mathematics Letters, 2013, 26(8): 886–892. doi: 10.1016/j.aml.2013.03.015
[6]	PELCKMANS K. Minlip for the identification of monotone Wiener systems[J]. Automatica, 2011, 47(10): 2298–2305. doi: 10.1016/j.automatica.2011.08.026
[7]	VÖRÖS J. Parameter identification of Wiener systems with multisegment piecewise-linear nonlinearities[J]. Systems & Control Letters, 2007, 56(2): 99–105. doi: 10.1016/j.sysconle.2006.08.001
[8]	YU Feng, MAO Zhizhong, and HE Dakuo. Identification of time-varying Hammerstein-Wiener systems[J]. IEEE Access, 2020, 8: 136906–136916. doi: 10.1109/ACCESS.2020.3011608
[9]	WANG Dongqing and DING Feng. Least squares based and gradient based iterative identification for Wiener nonlinear systems[J]. Signal Processing, 2011, 91(5): 1182–1189. doi: 10.1016/j.sigpro.2010.11.004
[10]	HAGENBLAD A, LJUNG L, and WILLS A. Maximum likelihood identification of Wiener models[J]. Automatica, 2008, 44(11): 2697–2705. doi: 10.1016/j.automatica.2008.02.016
[11]	HU Yuanbiao, LIU Baolin, ZHOU Qin, et al. Recursive extended least squares parameter estimation for wiener nonlinear systems with moving average noises[J]. Circuits, Systems, and Signal Processing, 2014, 33(2): 655–664. doi: 10.1007/s00034-013-9652-x
[12]	MZYK G and WACHEL P. Wiener system identification by input injection method[J]. International Journal of Adaptive Control and Signal Processing, 2020, 34(8): 1105–1119. doi: 10.1002/acs.3124
[13]	BAI Erwei. A blind approach to the Hammerstein–Wiener model identification[J]. Automatica, 2002, 38(6): 967–979. doi: 10.1016/S0005-1098(01)00292-8
[14]	LACY S L and BERNSTEIN D S. Identification of FIR Wiener systems with unknown, noninvertible, polynomial nonlinearities[C]. Proceedings of 2002 American Control Conference, Anchorage, USA, 2002: 893–898.
[15]	LJUNG L. System Identification[M]. WEBSTER J G. Wiley Encyclopedia of Electrical and Electronics Engineering. New York: John Wiley, 1999: 315–311. doi: 10.1002/047134608X.W1046.
[16]	孙明轩, 毕宏博. 学习辨识: 最小二乘算法及其重复一致性[J]. 自动化学报, 2012, 38(5): 698–706. doi: 10.3724/SP.J.1004.2012.00698 SUN Mingxuan and BI Hongbo. Learning identification: Least squares algorithms and their repetitive consistency[J]. Acta Automatica Sinica, 2012, 38(5): 698–706. doi: 10.3724/SP.J.1004.2012.00698
[17]	孙明轩, 毕宏博. 最小二乘学习辨识[C]. 第三十届中国控制会议, 烟台, 中国, 2011: 1615–1620. SUN Mingxuan and BI Hongbo. Least squares learning identification[C]. Proceedings of the 30th Chinese Control Conference, Yantai, China, 2011: 1615–1620.
[18]	HAMMAR K, DJAMAH T, and BETTAYEB M. Nonlinear system identification using fractional Hammerstein-Wiener models[J]. Nonlinear Dynamics, 2019, 98(3): 2327–2338. doi: 10.1007/s11071-019-05331-9
[19]	DING Feng, LIU P X, and LIU Guangjun. Gradient based and least-squares based iterative identification methods for OE and OEMA systems[J]. Digital Signal Processing, 2010, 20(3): 664–677. doi: 10.1016/j.dsp.2009.10.012
[20]	DING Feng, SHI Yang, and CHEN Tongwen. Performance analysis of estimation algorithms of nonstationary ARMA processes[J]. IEEE Transactions on Signal Processing, 2006, 54(3): 1041–1053. doi: 10.1109/TSP.2005.862845
[21]	DING Feng, XU Ling, MENG Dandan, et al. Gradient estimation algorithms for the parameter identification of bilinear systems using the auxiliary model[J]. Journal of Computational and Applied Mathematics, 2019, 369: 112575. doi: 10.1016/j.cam.2019.112575