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CSME 2018/02
Volume 39 No.1
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11-20
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Target Material Identification with High Pressure Water-jet Based on Wavelet Packet Decomposition and PSO-SVM
Hong-Tao Yanga, Wei Zhangb, Dong-Su Zhanga and Tian-Feng Wua
aSchool of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China.
bSchool of Instrument Science and Opto-Electronics Engineering, Hefei 230009, China.
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Abstract:
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 reflecti on 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 freque ncy bands sound signal was calculated as the eigenvalue for the SVM classification model. The standard particle swarm optimization algorithm ( was done in this paper, and the optimized PSO was used to optimize t he training parameter s ( penalty coefficie nt C and kernel function parameter σ) of the built SVM classification model . A s 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 t he classification accuracy (97.7 8%) was reached by using PSO SVM classification model , and the modell ing time is only 0.92 sec . 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) classificat ion model.
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Keywords: high pressure water-jet, material identification, wavelet packet decomposition, support vector machine, particle swarm optimization algorithm.
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*Corresponding author; e-mail:
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©
2018
CSME , ISSN 0257-9731
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