Identification of Hammerstein nonlinear ARMAX systems

被引：374
|
作者：
Ding, F
Chen, TW [1 ]
机构：
[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 2V4, Canada
[2] So Yangtze Univ, Control Sci & Engn Res Ctr, Wuxi 214122, Peoples R China
来源：
AUTOMATICA | 2005年 / 41卷 / 09期
基金：
加拿大自然科学与工程研究理事会;
关键词：
recursive identification; parameter estimation; convergence properties; stochastic gradient; least squares; Hammersteinm models; Wiener models; Martingale convergence theorem;
D O I：
10.1016/j.automatica.2005.03.026
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Two identification algorithms, an iterative least-squares and a recursive least-squares, are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models. The basic idea is to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates. Convergence properties of the recursive algorithm in the stochastic framework show that the parameter estimation error consistently converges to zero under the generalized persistent excitation condition. The simulation results validate the algorithms proposed. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：1479 / 1489
页数：11
相关论文
共 50 条
  • [1] Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms
    Chaudhary, Naveed Ishtiaq
    Raja, Muhammad Asif Zahoor
    NONLINEAR DYNAMICS, 2015, 79 (02) : 1385 - 1397
  • [2] Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms
    Naveed Ishtiaq Chaudhary
    Muhammad Asif Zahoor Raja
    Nonlinear Dynamics, 2015, 79 : 1385 - 1397
  • [3] Author's reply to "comments on 'identification of Hammerstein nonlinear ARMAX systems"'
    Ding, Feng
    Chen, Tongwen
    AUTOMATICA, 2007, 43 (08) : 1479 - 1479
  • [4] Comments on "Identification of Hammerstein nonlinear ARMAX systerns"
    Zhao, Wenxiao
    Fang, Haitao
    AUTOMATICA, 2007, 43 (08) : 1477 - 1478
  • [5] Gradient-based identification methods for Hammerstein nonlinear ARMAX models
    Ding, Feng
    Shi, Yang
    Chen, Tongwen
    NONLINEAR DYNAMICS, 2006, 45 (1-2) : 31 - 43
  • [6] A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system
    Ma, Liang
    Liu, Xinggao
    NONLINEAR DYNAMICS, 2015, 79 (02) : 1601 - 1613
  • [7] A nonlinear recursive instrumental variables identification method of Hammerstein ARMAX system
    Liang Ma
    Xinggao Liu
    Nonlinear Dynamics, 2015, 79 : 1601 - 1613
  • [8] Gradient-Based Identification Methods for Hammerstein Nonlinear ARMAX Models
    Feng Ding
    Yang Shi
    Tongwen Chen
    Nonlinear Dynamics, 2006, 45 : 31 - 43
  • [9] Expectation-maximization algorithm based identification of Hammerstein nonlinear ARMAX systems with Gaussian mixture noises
    Jin, Qibing
    Xing, Yunfei
    Du, Xinghan
    Niu, Yaxu
    Cai, Wu
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 1746 - 1751
  • [10] Identification for Hammerstein nonlinear ARMAX systems based on multi-innovation fractional order stochastic gradient
    Cheng, Songsong
    Wei, Yiheng
    Sheng, Dian
    Chen, Yuquan
    Wang, Yong
    SIGNAL PROCESSING, 2018, 142 : 1 - 10
12345