In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is proposed. Firstly, a multi-input and multioutput linear regression model of the multipoint frequency domain vibration response is trained using historical data at each frequency point. Secondly, the trained model under each frequency point is improved by the elastic regularization. Finally, the model is used in a working situation. The predicted vibration response on the experimental dataset of cylindrical shell acoustic vibration showed that the improvement of the multivariate regression vibration response prediction model by elastic regularization can better improve the accuracy and reduce the large number of conditions at some frequencies.
Multipoint Vibration Response Prediction under Uncorrelated Multiple Sources Load Based on Elastic-Net Regularization in Frequency Domain
