Recursive identification for Wiener systems using Gaussian inputs

被引：7
|
作者：
Hu, Xiao-Li [1 ]
Chen, Han-Fu [2 ]
机构：
[1] China Jiliang Univ, Dept Math, Coll Sci, Hangzhou 310018, Zhejiang, Peoples R China
[2] Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
来源：
ASIAN JOURNAL OF CONTROL | 2008年 / 10卷 / 03期
关键词：
Wiener system; nonparametric nonlinearity; recursive estimate; strong consistency; stochastic approximation;
D O I：
10.1002/asjc.27
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The recursive algorithms are given for identifying the single-input single-output Wiener system which consists of a moving average type linear subsystem followed by a static nonparametric nonlinearity. The input is defined to be a sequence of mutually independent Gaussian random variables. The estimates for coefficients of the linear subsystem as well as for f(upsilon) at any upsilon are proved to converge to the true values with probability one. A numerical example is given. justifying the theoretical analysis.
引用
下载
收藏
页码：341 / 350
页数：10
相关论文
共 50 条
  • [21] Recursive identification method for a class of Hammerstein-Wiener systems
    Yu, Feng
    Mao, Zhi-Zhong
    Jia, Ming-Xing
    Yuan, Ping
    Yang, Fei-Sheng
    Zidonghua Xuebao/Acta Automatica Sinica, 2014, 40 (02): : 327 - 335
  • [22] Recursive Identification for Semiparametric Multi-channel Wiener Systems
    Chen, Xing-Min
    2014 33RD CHINESE CONTROL CONFERENCE (CCC), 2014, : 6786 - 6791
  • [23] Information matrix and D-optimal design with Gaussian inputs for Wiener model identification
    Mahata, Kaushik
    Schoukens, Johan
    De Cock, Alexander
    AUTOMATICA, 2016, 69 : 65 - 77
  • [24] RECURSIVE PREDICTION ERROR IDENTIFICATION USING THE NONLINEAR WIENER MODEL
    WIGREN, T
    AUTOMATICA, 1993, 29 (04) : 1011 - 1025
  • [25] Recursive identification of errors-in-variables Wiener-Hammerstein systems
    Mu, Bi-Qiang
    Chen, Han-Fu
    EUROPEAN JOURNAL OF CONTROL, 2014, 20 (01) : 14 - 23
  • [26] The recursive least squares identification algorithm for a class of Wiener nonlinear systems
    Ding, Feng
    Liu, Ximei
    Liu, Manman
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2016, 353 (07): : 1518 - 1526
  • [27] Recursive parameter identification of Hammerstein-Wiener systems with measurement noise
    Yu, Feng
    Mao, Zhizhong
    Jia, Mingxing
    Yuan, Ping
    SIGNAL PROCESSING, 2014, 105 : 137 - 147
  • [28] Recursive identification of Wiener systems with two-segment polynomial nonlinearities
    Voeroes, Jozef
    JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2008, 59 (01): : 40 - 44
  • [29] Online state and inputs identification for stochastic systems using recursive expectation-maximization algorithm
    Liu, Zhuangyu
    Zhao, Shunyi
    Luan, Xiaoli
    Liu, Fei
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2021, 217
  • [30] Erratum: Stochastic analysis of adaptive gradient identification of Wiener-Hammerstein systems for Gaussian inputs (IEEE Transactions on Signal Processing)
    Nollett, B.S.
    Bershad, N.J.
    IEEE Transactions on Signal Processing, 2001, 49 (09)
12345