350 rub
Journal Neurocomputers №3 for 2018 г.
Article in number:
Criteria for minimal RBF artificial neural network’s center vectors set definition
Type of article:
scientific article
UDC: 004.931, 621.372
Keywords:
artificial neural network
training algorithm for artificial neural network
kronecker-capelli theorem
rbf ann center vectors.
Authors:
M.A. Abrosimov − Post-graduate Student, Department of Applied Information Technologies, Yuri Gagarin State Technical University of Saratov (SSTU)
E-mail: destinywatcher@gmail.com
A.V. Brovko − Dr.Sc. (Phys.-Math.), Professor, Department of Applied Information Technologies, Yuri Gagarin State Technical University of Saratov (SSTU)
E-mail: brovkoav@gmail.com
Abstract:
This paper points out the problem of defining the center vectors set of RNF artificial neural network. The criteria for sufficient center vectors count is presented. The results of numerical modeling, presented in the paper, confirm a significant reduction of RBF ANN training time with no information loss in hidden layer
Pages: 50-54
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Date of receipt: 31 июля 2017 г.