Modelling of explosive cutting process of plates using GMDH-type neural network and singular value decomposition

Group method of data handling (GMDH)-type neural networks are used for modelling of explosive cutting process of plates by shaped charges. The aim of such modelling is to show how the depth of penetration varies with the variation of important parameters. It is also demonstrated that singular value decomposition (SVD) can be effectively used to find the vector of coefficients of a quadratic sub-expression embodied in such GMDH-type networks. Such application of SVD will highly improve the performance of GMDH-type networks to model the very complex process of explosive cutting of plates by shaped charges.