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Prediction of acceleration peak based on radial basis function network


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Prediction of acceleration peak based on radial basis function network
Liu WeiSun XiaoxiaShen RuiqiYe YinghuaLi Chuangxin
School of Chemical Engineering,NUST,Nanjing 210094,China
impact dynamic Hopkinson pressure bars acceleration radial basis function
In order to predict the peak,type and duration of acceleration more exactly,a novel method is proposed based on the radial basis function(RBF)neural network model for the free Hopkinson pressure bar overload technique.Pulse shaper experiments are carried out under different striking velocities and sizes of the LY12 aluminium shaper,and acceleration data are obtained and normalized to(0,1)and used for the RBF network learning.Five sample data are selected randomly to predict the acceleration peaks with the trained network.It is concluded that the presented network is credible to predict the peak and duration of acceleration according to the striking velocity and the size of the shaper.


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Last Update: 2013-10-31