This paper investigates the utilisation of feedforward and recurrent neural networks for dynamic modelling of a flexible plate structure. Neuro-modelling techniques are used for non-parametric identification of the flexible plate structure based on one-step-ahead prediction. A multi layer perceptron (MLP) and Elman neural networks are designed to characterise the dynamic behaviour of the flexible plate. Results of the modelling techniques are validated through a range of tests including input/output mapping, training and test validation, mean-squared error and correlation tests. Results are presented in both time and frequency domains. Comparative performance assessments of both neuro-modelling approaches in terms of mean-squared error and estimation of the resonance modes of the system are carried out. It is noted that both techniques have been able to detect the first five vibration modes of the system successfully. Investigations also signify the advantage of a recurrent Elman network over an MLP feedforward network in modelling the flexible plate structure.

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