State of the art in nonlinear dynamical system identification using Artificial Neural Networks

被引:0
|
作者
Todorovic, Nenad
Klan, Petr
机构
关键词
artificial neural networks; nonlinear dynamical systems; nonlinear identification;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper covers the state of the art in nonlinear dynamical system identification using Artificial Neural Networks (ANN). The main approaches in the last two decades are presented in unified framework. ANN have unique characteristics, which enable them to model nonlinear dynamical systems. The main problems with the choice of ANN model structure are considered and commonly used identification schemes are proposed. A procedure for derivation of parameter estimation law using Lyapunov synthesis approach, which guarantees stability and convergence of the overall identification scheme, is presented.
引用
收藏
页码:103 / 108
页数:6
相关论文
共 50 条
  • [21] A class of type-2 fuzzy neural networks for nonlinear dynamical system identification
    Jafar Tavoosi
    Mohammad Ali Badamchizadeh
    [J]. Neural Computing and Applications, 2013, 23 : 707 - 717
  • [22] Artificial neural networks and bankruptcy forecasting: a state of the art
    Muriel Perez
    [J]. Neural Computing & Applications, 2006, 15 : 154 - 163
  • [23] Hydrological forecasting with artificial neural networks:: The state of the art
    Coulibaly, P
    Anctil, F
    Bobée, B
    [J]. CANADIAN JOURNAL OF CIVIL ENGINEERING, 1999, 26 (03) : 293 - 304
  • [24] Artificial neural networks and bankruptcy forecasting: a state of the art
    Perez, M
    [J]. NEURAL COMPUTING & APPLICATIONS, 2006, 15 (02): : 154 - 163
  • [25] System Identification of Nonlinear Inverted Pendulum Using Artificial Neural Network
    Gautam, Pooja
    [J]. 2016 INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2016,
  • [26] NONLINEAR MODELING AND CONTROLLING OF ARTIFICIAL MUSCLE SYSTEM USING NEURAL NETWORKS
    Tian Sheping
    Ding Guoqing
    Yan Detian
    Lin Liangming Department of Information Measurement and Instrumentation
    [J]. Chinese Journal of Mechanical Engineering, 2004, (02) : 306 - 310
  • [27] Identification of nonlinear dynamic systems using functional link artificial neural networks
    Patra, JC
    Pal, RN
    Chatterji, BN
    Panda, G
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1999, 29 (02): : 254 - 262
  • [28] Identification of power system load dynamics using artificial neural networks
    Bostanci, M
    Koplowitz, J
    Taylor, CW
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1997, 12 (04) : 1468 - 1473
  • [29] System Identification in Difficult Operating Conditions Using Artificial Neural Networks
    Matic, Petar
    Medvesek, Ivana Golub
    Peric, Tina
    [J]. TRANSACTIONS ON MARITIME SCIENCE-TOMS, 2015, 4 (02): : 105 - 112
  • [30] Electric Transmission System Fault Identification Using Artificial Neural Networks
    Asbery, Christopher W.
    Liao, Yuan
    [J]. 2019 INTERNATIONAL ENERGY AND SUSTAINABILITY CONFERENCE (IESC), 2019,