Identification of Wiener-Hammerstein systems using the best linear approximation

被引:0
|
作者
Lauwers L. [1 ]
Schoukens J. [1 ]
机构
[1] Vrije Universiteit Brussel, Brussels B-1050
关键词
Biological systems;
D O I
10.1007/978-1-84996-513-2_13
中图分类号
学科分类号
摘要
Nonlinear block-oriented models have successfully been used in many engineering applications to identify chemical, mechanical, and biological systems or processes. Due to their simple structure, these models are very attractive from a user's point of view. However, the block-oriented approach also has some disadvantages over black-box modelling approaches. © 2010 Springer London.
引用
收藏
页码:209 / 225
页数:16
相关论文
共 50 条
  • [41] A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener-Hammerstein Models by Using WH-EA and Multistep Signals
    Zambrano, J.
    Sanchis, J.
    Herrero, J.M.
    Martínez, M.
    Complexity, 2020, 2020
  • [42] Identification of a Wiener-Hammerstein system using an incremental nonlinear optimisation technique
    Tan, Ai Hui
    Wong, Hin Kwan
    Godfrey, Keith
    CONTROL ENGINEERING PRACTICE, 2012, 20 (11) : 1140 - 1148
  • [43] Identification for Wiener-Hammerstein systems under quantized inputs and quantized output observations
    Guo, Jin
    Zhao, Yanlong
    ASIAN JOURNAL OF CONTROL, 2021, 23 (01) : 118 - 127
  • [44] Convex Relaxation Approach to the Identification of the Wiener-Hammerstein Model
    Sou, Kin Cheong
    Megretski, Alexandre
    Daniel, Luca
    47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008), 2008, : 1375 - 1382
  • [45] Wiener-Hammerstein model identification-recursive algorithms
    Emara-Shabaik, HE
    Ahmed, MS
    Al-Ajmi, KH
    JSME INTERNATIONAL JOURNAL SERIES C-MECHANICAL SYSTEMS MACHINE ELEMENTS AND MANUFACTURING, 2002, 45 (02) : 606 - 613
  • [46] Identification of Wiener-Hammerstein systems by l1 -constrained Volterra series
    Lagosz, S.
    Sliwinski, P.
    Wachel, P.
    EUROPEAN JOURNAL OF CONTROL, 2021, 58 : 53 - 59
  • [47] Wiener-Hammerstein systems modeling using diagonal Volterra kernels coefficients
    Kibangou, AY
    Favier, G
    IEEE SIGNAL PROCESSING LETTERS, 2006, 13 (06) : 381 - 384
  • [48] Kernel-based identification of Wiener-Hammerstein system
    Mzyk, Grzegorz
    Wachel, Pawel
    AUTOMATICA, 2017, 83 : 275 - 281
  • [49] Structure Detection of Wiener-Hammerstein Systems With Process Noise
    Zhang, Erliang
    Schoukens, Maarten
    Schoukens, Johan
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2017, 66 (03) : 569 - 576
  • [50] Stochastic analysis of adaptive gradient identification of Wiener-Hammerstein systems for Gaussian inputs
    Bershad, NJ
    Bouchired, S
    Castanie, F
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2000, 48 (02) : 557 - 560