Wiener System Identification using Polynomial Non Linear State Space Model

被引：0
|
作者：
Lamia, Sersour [1 ]
Djamah, Tounsia [1 ]
Hammar, Karima [1 ]
Bettayeb, Maamar [2 ]
机构：
[1] Univ M Mammeri Tizi Ouzou L2CSP, Tizi Ouzou, Algeria
[2] Univ Sharjah, Dept Elect Comp Engn, Sharjah, U Arab Emirates
来源：
3RD INTERNATIONAL CONFERENCE ON CONTROL, ENGINEERING & INFORMATION TECHNOLOGY (CEIT 2015) | 2015年
关键词：
Non-linear System; Identification; Wiener model; Polynomial Non Linear State Space(PNLSS) model; Levenberg-Marquardt (LM) algorithm; NONLINEAR DYNAMIC-SYSTEMS; ALGORITHM;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with identification of Wiener non-linear systems. Such systems, consist of a linear dynamic block followed by a static non-linear subsystem. In this work, Polynomial Non Linear State Space(PNLSS) models are used to describe them. An output error identification method is performed, based on Levenberg-Marquardt algorithm; the parameters sensitivity functions are developed as a multivariable state space model. The method efficiency is investigated on numerical simulations in absence of noise and with noisy data for different signal to noise ratios.
引用
收藏
页数：5
相关论文
共 50 条
  • [1] Fractional Hammerstein system identification using polynomial non-linear state space model
    Hammar, Karima
    Djamah, Tounsia
    Bettayeb, Maamar
    [J]. 3RD INTERNATIONAL CONFERENCE ON CONTROL, ENGINEERING & INFORMATION TECHNOLOGY (CEIT 2015), 2015,
  • [2] Identification of a Wiener-Hammerstein system using the polynomial nonlinear state space approach
    Paduart, J.
    Lauwers, L.
    Pintelon, R.
    Schoukens, J.
    [J]. CONTROL ENGINEERING PRACTICE, 2012, 20 (11) : 1133 - 1139
  • [3] Recursive subspace identification of linear and non-linear Wiener state-space models
    Lovera, M
    Gustafsson, T
    Verhaegen, M
    [J]. AUTOMATICA, 2000, 36 (11) : 1639 - 1650
  • [4] Parameter identification for Hammerstein nonlinear system with polynomial and state space model
    Li, Chenghao
    Li, Feng
    Cao, Qingfeng
    [J]. MEASUREMENT & CONTROL, 2023, 56 (1-2): : 327 - 336
  • [5] Polynomial Nonlinear Fractional State Space System Identification
    Marzougui, Soumaya
    Atitallah, Asma
    Bedoui, Saida
    Abderrahim, Kamel
    [J]. 2019 19TH INTERNATIONAL CONFERENCE ON SCIENCES AND TECHNIQUES OF AUTOMATIC CONTROL AND COMPUTER ENGINEERING (STA), 2019, : 286 - 291
  • [6] Identification for Polynomial Wiener Model Using Improved Back Propagation Method
    Shao, Xiaobing
    [J]. 2012 INTERNATIONAL CONFERENCE ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING (ICMPBE2012), 2012, 33 : 718 - 725
  • [7] Identification for Polynomial Wiener Model Using Improved Back Propagation Method
    Shao, Xiaobing
    [J]. 2010 INTERNATIONAL COLLOQUIUM ON COMPUTING, COMMUNICATION, CONTROL, AND MANAGEMENT (CCCM2010), VOL III, 2010, : 418 - 421
  • [8] Identification of the Linear Dynamic Parts of Wiener Model Using Kernel and Linear Adaptive
    Fateh, Rachid
    Darif, Anouar
    Safi, Said
    [J]. ADVANCED INTELLIGENT SYSTEMS FOR SUSTAINABLE DEVELOPMENT (AI2SD'2020), VOL 2, 2022, 1418 : 387 - 400
  • [9] An evolutionary approach to non-linear polynomial system identification
    Rodríguez-Vázquez, K
    Fonseca, CM
    Fleming, PJ
    [J]. (SYSID'97): SYSTEM IDENTIFICATION, VOLS 1-3, 1998, : 1339 - 1344
  • [10] Identification of statistic equivalent polynomial non-linear system
    Qiu, Yang
    Zhang, Jinghui
    [J]. Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 1993, 27 (06): : 1 - 8
12345