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Identification of structural systems by neural networks

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  • Chassiakos, Anastassios G.
  • Masri, Sami F.

Abstract

A method based on the use of neural networks is developed for the identification of systems encountered in the field of structural dynamics. The methodology is applied to the identification of linear and nonlinear dynamic systems such as the damped Duffing oscillator and the Van der Pol equation. The “generalization” ability of the neural networks is used to predict the response of the identified systems under deterministic and stochastic excitations. It is shown that neural networks provide high fidelity models of unknown structural dynamic systems, which are used in applications such as structural control, health monitoring of structures, earthquake engineering, etc.

Suggested Citation

  • Chassiakos, Anastassios G. & Masri, Sami F., 1996. "Identification of structural systems by neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 40(5), pages 637-656.
    • Handle: RePEc:eee:matcom:v:40:y:1996:i:5:p:637-656
    DOI: 10.1016/0378-4754(95)00012-7
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    Cited by:

    1. Banerjee, Amit & Abu-Mahfouz, Issam, 2014. "A comparative analysis of particle swarm optimization and differential evolution algorithms for parameter estimation in nonlinear dynamic systems," Chaos, Solitons & Fractals, Elsevier, vol. 58(C), pages 65-83.
    2. Zand, Behnam & Ghaderi, Pedram & Amini, Fereidoun, 2023. "Structural system identification via synchronization technique and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 203(C), pages 174-188.

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