Neural networks committee decision making for estimation of metal's hardness properties from indentation data

Authors:
O. A. Mishulina;I. A. Kruglov;M. B. Bakirov
Affiliations:
National Nuclear Research University "MEPhI", Moscow, Russia 115409;National Nuclear Research University "MEPhI", Moscow, Russia 115409;Center of Material Science and Resource, Ljubertsi, Moscow oblast, Russia 140002
Venue:
Optical Memory and Neural Networks
Year:
2011

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Abstract

In this paper the problem of metal's hardness properties estimation from indentation data is concerned. This problem belongs to a class of ill-posed vector function approximation problems and can't be solved by a single multilayered perceptron at the required precision level. A special neural networks committee architecture is developed in order to obtain precise estimates of metal's hardness properties. This method involves well-posed direct indentation task solution and a quantile based idea for best estimates selection. Studies have shown the estimates produced by the committee to be stable even in the case of noise presence that is similar to the true experimental one.