Abstract
On one hand,it has been demonstrated theoretically and verified numerically that neural networks can act as a time domain nonparametric modeling approach of engineering dynamic systems by forecasting their dynamic responses according to them in the past consequent time steps. On the other hand, as a time-domain auto-regressive method, the auto-regressive and moving average (ARMA) model has been widely employed to describe the mapping between structural dynamics response at a current time instant and them in the past previous time instants. The equivalency of the physical meaning of the neural network nonparametric model and the ARMA parametric model for dynamic systems is testified firstly in this paper. Then, a novel structural parametric identification methodology based on the nonparametric neural network model is proposed by the use of excitation and dynamic response measurement time series. The accuracy and efficacy of the proposed strategy for a multi-storey frame structure model are validated using the excitation and acceleration measurement time series of impact test.
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Xu, B., Gong, A., He, J., Masri, S.F. (2009). A Novel Time-Domain Structural Parametric Identification Methodology Based on the Equivalency of Neural Networks and ARMA Model. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_94
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DOI: https://doi.org/10.1007/978-3-642-04070-2_94
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