Application of neural network model with partial least-squares regression in prediction of peak velocity of blasting vibration

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Journal of Vibration and Shock ›› 2013, Vol. 32 ›› Issue (12) : 45-49.

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Application of neural network model with partial least-squares regression in prediction of peak velocity of blasting vibration

SHI Xiu-zhi,WU Yong-meng, TANG Li-zhong，HUANG Xuan-dong

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Abstract

The neural network method is a powerful tool to solve the problem of nonlinearity，but when the input variables are very large,the multicollinearity among variables will lead low modeling efficiency.Partial least-squares regression(PLSR) method can extract compositions with better interpretation to the dependent variables,and it can preferably overcome the multicollinearity among variables. By combining the two methods, a BP neural network prediction model for peak velocity of blasting vibration based on PLSR was established. The affecting factors for blasting vibration were analyzed by means of PLSR,and three new synthesis variables were extracted. Since input layers of BP neural network were decreased from nine to three,the network structure is simpler,more efficient and more stable.The results show that the average prediction errors of coupled model is 7.62%,which is more accurate than Sadaovsk formula and normal BP neural network model.

Key words

blasting vibration velocity / multicollinearity / partial least-squares regression / BP neural network

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SHI Xiu-zhi;WU Yong-meng;TANG Li-zhong;HUANG Xuan-dong. Application of neural network model with partial least-squares regression in prediction of peak velocity of blasting vibration[J]. Journal of Vibration and Shock, 2013, 32(12): 45-49