Nonlinear dynamic modelling of flexible beam structures using neural networks

被引:0
|
作者
Hashim, SZM [1 ]
Tokhi, MO [1 ]
Darus, IZM [1 ]
机构
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England
关键词
system identification; neural networks; flexible beam; non-linear system;
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 investigates the utilisation of back propagation neural networks (NNs) for modelling flexible beam structures in fixed-free mode; a simple repsentation of an aircraft wing or robot arm. A comparative performance of the NN model and conventional recursive least square scheme, in characterising the system is carried out in the time and frequency domains. Simulated results demonstrate that using NN approach the system is modelled better than with the conventional linear modelling approach. The developed neuro-modelling approach will further be utilized in the design and implementation of suitable controllers, for vibration suppression in such system.
引用
收藏
页码:171 / 175
页数:5
相关论文
共 50 条
  • [41] Control of Nonlinear Dynamic Systems Using Neural Networks with Incremental Learning
    Moran, Antonio
    CONFERENCE PROCEEDINGS OF 2018 4TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2018, : 182 - 189
  • [42] Nonlinear filtering design using dynamic neural networks with fast training
    Becerikli, Y
    COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, 2003, 2869 : 601 - 610
  • [43] Identification of nonlinear dynamic systems using diagonal recurrent neural networks
    Wang, Jing
    Chen, Hui
    Journal of University of Science and Technology Beijing: Mineral Metallurgy Materials (Eng Ed), 1999, 6 (02): : 149 - 151
  • [44] Performance analysis of 4 types of conjugate gradient algorithms in the nonlinear dynamic modelling of a TRMS using feedforward neural networks
    Shaheed, MH
    2004 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN & CYBERNETICS, VOLS 1-7, 2004, : 5985 - 5990
  • [45] Modelling Semiconductor Junctions Including Nonlinear Capacitive Effects using Neural Networks
    Gunupudi, P.
    Tang, P.
    Zhang, Q. J.
    Smy, T.
    2011 15TH IEEE WORKSHOP ON SIGNAL PROPAGATION ON INTERCONNECTS (SPI), 2011, : 137 - 138
  • [46] Identification of nonlinear hysteretic structures by neural networks
    He, W
    Ma, F
    Ng, CN
    ISTM/2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings, 2005, : 5056 - 5059
  • [47] Simulation of nonlinear structures with artificial neural networks
    Paez, TL
    ENGINEERING MECHANICS: PROCEEDINGS OF THE 11TH CONFERENCE, VOLS 1 AND 2, 1996, : 72 - 75
  • [48] Neural networks for nonlinear identification and diagnosis of structures
    Kao, CY
    Tseng, CC
    Loh, CC
    Wu, TH
    STRUCTURAL HEALTH MONITORING AND INTELLIGENT INFRASTRUCTURE, VOLS 1 AND 2, 2003, : 619 - 627
  • [49] Application of neural networks in beam emission spectroscopy modelling
    Jalalvand, Azarakhsh
    Asztalos, Ors
    Karacsonyi, Mate
    Pokol, Gergo I.
    2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
  • [50] Dynamic modelling of competitive elution of activated carbon in columns using neural networks
    vanDeventer, JSJ
    Liebenberg, SP
    Lorenzen, L
    Aldrich, C
    MINERALS ENGINEERING, 1995, 8 (12) : 1489 - 1501