Neural network modeling of vector multivariable functions in ill-posed approximation problems

Authors:
I. A. Kruglov;O. A. Mishulina
Affiliations:
National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia 115409;National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russia 115409
Venue:
Journal of Computer and Systems Sciences International
Year:
2013

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Abstract

A neural network solution of the ill-posed inverse approximation problem of a multivariable vector function based on of a committee of multilayer perceptrons is proposed. A nonlinear adaptive decision-making rule by the committee is developed that improves the accuracy compared with other neural network solutions of the inverse problem. Using a model example, the accuracy characteristics of the method are shown. An applied engineering problem is considered and the results of its solution by the proposed method are presented.