Research on Artificial Neural Network-based Prediction Model for the Critical Thickness of RDX

Abstract

Abstract: The critical thickness of RDX at different densities, grain sizes and binding content was tested using a wedge-shaped charge. The Elman prediction model was established using density, grain size and binding content as input variables, and the critical thickness of RDX as output variable to predict the critical thickness under other conditions. The results show that the relationship between the three factors and the critical thickness is the same as that in Ref.［9-11］, and the neural network can be used to predict the critical thickness of RDX.

Key words: explosion mechanics, RDX, critical thickness, Elman neural network

CLC Number:

TJ55

LIU Yu-cun₁, YU Guo-qiang, DONG Guo-qing₃. Research on Artificial Neural Network-based Prediction Model for the Critical Thickness of RDX[J]. Acta Armamentarii, 2010, 31(10): 1394-1397.

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