IDENTIFICATION OF NONLINEAR-SYSTEM STRUCTURE AND PARAMETERS USING REGIME DECOMPOSITION

被引：154
|
作者：
JOHANSEN, TA
FOSS, BA
机构：
[1] Department of Engineering Cybernetics, Norwegian Institute of Technology, The University of Trondheim
来源：
AUTOMATICA | 1995年 / 31卷 / 02期
关键词：
NONLINEAR SYSTEMS; SYSTEM IDENTIFICATION; MODELING; PROCESS MODELS; FERMENTATION PROCESSES;
D O I：
10.1016/0005-1098(94)00096-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An off-line algorithm for empirical modeling and identification of non-linear dynamic systems is presented. The minimal input to the algorithm is a sequence of empirical data and the model order. Using this information, the algorithm searches for an optimal model structure and parameters within a rich non-linear model set. The model representation is based on the interpolation of a number of simple local models, where each local model has a limited range of validity, but the local models yield a complete global model when interpolated. The method is illustrated using simulated data.
引用
收藏
页码：321 / 326
页数：6
相关论文
共 50 条
  • [41] Nonlinear dynamic system identification using least squares support vector machine regression
    Wang, XD
    Ye, MY
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2004, : 941 - 945
  • [42] Nonlinear system identification with model structure selection via distributed computation
    Bianchi, Federico
    Falsone, Alessandro
    Prandini, Maria
    Piroddi, Luigi
    2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 6461 - 6466
  • [43] Nonlinear thermal system identification using fractional Volterra series
    Maachou, Asma
    Malti, Rachid
    Melchior, Pierre
    Battaglia, Jean-Luc
    Oustaloup, Alain
    Hay, Bruno
    CONTROL ENGINEERING PRACTICE, 2014, 29 : 50 - 60
  • [44] Identification of nonlinear dynamic system using machine learning techniques
    Samal D.
    Bisoi R.
    Sahu B.
    International Journal of Power and Energy Conversion, 2021, 12 (01) : 23 - 43
  • [45] Nonlinear System Identification Using a Subband Adaptive Volterra Filter
    Burton, T.
    Beaucoup, F.
    Goubran, R.
    2008 IEEE INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, VOLS 1-5, 2008, : 939 - +
  • [46] An improved approach for nonlinear system identification using neural networks
    Gupta, P
    Sinha, NK
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 1999, 336 (04): : 721 - 734
  • [47] Nonlinear System Identification Using a Subband Adaptive Volterra Filter
    Burton, Trevor G.
    Goubran, Rafik A.
    Beaucoup, Franck
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2009, 58 (05) : 1389 - 1397
  • [48] Blind identification of an autoregressive system using a nonlinear dynamical approach
    Leung, H
    Wang, SC
    Chan, AM
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2000, 48 (11) : 3017 - 3027
  • [49] Nonlinear system identification of a MIMO quadruple tanks system using NARX model
    Araujo, Icaro
    Cavalcante, Gabriel
    Lucio, Yan
    Araujo, Fabio
    PRZEGLAD ELEKTROTECHNICZNY, 2019, 95 (06): : 66 - 72
  • [50] Nonlinear System Identification Using Deep Learning and Randomized Algorithms
    de la Rosa, Erick
    Yu, Wen
    Li, Xiaoou
    2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, 2015, : 274 - 279
12345