Principal component analysis method of parameters for network prediction of ship impact environment
Aiming at the problem of low accuracy in shock environment prediction using neural networks because of the strong nonlinear characteristics of ship underwater explosion shock,a method based on principal component a-nalysis is used to improve accuracy by downscaling the input parameters of the network model.Using mathematical matrix eigenvalue extraction and matrix transformation,original data samples are subjected to dimensionality reduc-tion by principal component analysis and factor analysis.Then,the adapted network is selected for the fast forecas-ting of shock spectral values.The experimental results show that the selection of principal components mainly con-siders the size and decreasing trend of the eigenvalues,retains the eigenvalues of the steeply decreasing section,and analyzes the trade-offs of the eigenvalues of the transition section.Meanwhile,the implementation of the decor-relation and dimensionality reduction processing on the parameters can significantly improve the forecasting accuracy of the neural network.
万方数据
维普
