GMDH-Type neural network modelling of explosive cutting process of plates using singular value decomposition

Authors:
A. Darvizeh;N. Nariman-Zadeh;H. Gharababaei
Affiliations:
Department of Mechanical Engineering, Technical Faculty, Guilan University, P.O. Box 3756, Rasht, Iran;Department of Mechanical Engineering, Technical Faculty, Guilan University, P.O. Box 3756, Rasht, Iran;Department of Mechanical Engineering, Technical Faculty, Guilan University, P.O. Box 3756, Rasht, Iran
Venue:
Systems Analysis Modelling Simulation - Special issue: Self-organising modelling and simulation
Year:
2003

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Abstract

GMDH-type neural networks are used for modelling and prediction 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.