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Advances in Electrical Engineering and Computational Science

Volume 39 of the series Lecture Notes in Electrical Engineering pp 377-388

Multilayer Perceptron Training Optimization for High Speed Impacts Classification

  • Angel Garcia-CrespoAffiliated withDepartment of Computer Science, Universidad Carlos III
  • , Belen Ruiz-MezcuaAffiliated withDepartment of Computer Science, Universidad Carlos III
  • , Israel Gonzalez-CarrascoAffiliated withDepartment of Computer Science, Universidad Carlos III
  • , Jose Luis Lopez-CuadradoAffiliated withDepartment of Computer Science, Universidad Carlos III

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The construction of structures subjected to impact was traditionally carried out empirically, relying on real impact tests. The need for design tools to simulate this process triggered the development in recent years of a large number of models of different types. Taking into account the difficulties of these methods, poor precision and high computational cost, a neural network for the classification of the result of impacts on steel armours was designed. Furthermore, the numerical simulation method was used to obtain a set of input patterns to probe the capacity of themodel development. In the problem tackled with, the available data for the network designed include, the geometrical parameters of the solids involved — radius and length of the projectile, thickness of the steel armour — and the impact velocity, while the response of the system is the prediction about the plate perforation.

Keywords

Impacts Classification Multilayer Perceptron Optimization neural network simulation