A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates

Naskar, Susmita, Mukhopadhya, Tanmoy and Sriramula, S. (2019) A comparative assessment of ANN and PNN model for low-frequency stochastic free vibration analysis of composite plates. In, Samui, Pijush, Chakraborty, Subrata, Tien Bui, Dieu and Deo, Ravinesh C. (eds.) Handbook of Probabilistic Models. Elsevier. (doi:10.1016/B978-0-12-816514-0.00022-9).

Abstract

This chapter quantifies the effect of uncertainty in natural frequencies of laminated composite plates based on neural network–based approach coupled with finite element analysis. An exhaustive comparative investigation on the performance of artificial neural network and polynomial neural network is carried out from the viewpoint of accuracy and computational efficiency. The stochastic system parameters are modeled following a layer-wise random variable–based approach, where the random system properties are considered to be different at different layers of the laminate for a particular realization of Monte Carlo simulation (MCS). Both individual and combined variations of stochastic input parameters are considered to address the aspect of low and high dimensional input parameter spaces, respectively. The convergence of the proposed neural network–based algorithm is verified and validated with original finite element method and direct MCS.

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Contributors

Author: Susmita Naskar

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