Target Material Identification with High Pressure Water-jet Based on Wavelet Packet Decomposition and PSO-SVM

In order to classify the target's material by using the reflection sound signal generated while the target was impacted by the high pressure water-jet, the reflection sound signal was pre-processed and decomposed by wavelet packet in this paper, and the optimum frequency bands of the reflection sound signal was selected through comparative experiments. The relative energy distribution of the optimally selected frequency bands sound signal was calculated as the eigenvalue for the SVM classification model. The standard particle swarm optimization algorithm (PSO) was done in this paper, and the optimized PSO was used to optimize the training parameters (penalty coefficient C and kernel function parameter sigma) of the built SVM classification model. As a result, the classification accuracy of the PSO-SVM classification model can be improved, and the time of parameter optimization was reduced. The experimental results show that the classification accuracy (97.78%) was reached by using PSO-SVM classification model, and the modelling time is only 0.92sec. The overall classification accuracy of PSO-SVM classification model was apparently higher than that of BPN, PNN and SVM (K-CV, LOOCV and Grid Search) classification model.