Prediction of Sound Insulation Performance of Composite Laminated Plates Based on Artificial Neural Network
Facing the demand of sound insulation design of fiber reinforced composite plates,18 groups of symmetrically laid composite laminates are designed by orthogonal test method,and their sound transmission losses in the frequency range of 10-1 500 Hz are respectively predicted by a semi-analytical method based on Chebyshev polynomial expansion.Aiming at a fast and accurate prediction and optimization of sound transmission loss of the composite laminates,The prediction models of Back Propagation(BP)neural network,Radial Basis Function(RBF)neural network and General Regression(GR)neural network are respectively established by taking the lamina thickness and the fiber angles as the input of artificial neural networks,and 1/3 octave sound transmission loss of composite laminates as the output.The results show that RBF neural network has the best predictive performance,and its root mean square error is only 1.093 7.Both of BP and GR neural networks have lower prediction effects than RBF neural network,and their root mean square errors are respectively 2.969 7 and 2.649 9.Finally,based on the analysis results,a RBF neural network model with good local prediction performance is constructed for the prediction of sound insulation performance of composite laminates particularly in low-frequency range.
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