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This article is cited in 2 scientific papers (total in 2 papers)
MATHEMATICAL MODELING AND NUMERICAL SIMULATION
Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior
A. G. Shumixin, A. S. Boyarshinova Perm National Research Polytechnic University, 29, Komsomolsky pr., Perm, 614000, Russia
Abstract:
The article presents an approach to configuration of an artificial neural network architecture and a training set size. Configuration is based on parameter minimization with constraints specifying neural network model quality criteria. The algorithm of artificial neural network architecture and training set size configuration is applied to dynamic object artificial neural network approximation. Series of computational experiments were performed. The method is applicable to construction of dynamic object models based on non-linear autocorrelation neural networks.
Keywords:
dynamic object model, training set, artificial neural network, architecture, training, optimization of artificial neural network architecture.
Received: 03.02.2015
Citation:
A. G. Shumixin, A. S. Boyarshinova, “Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior”, Computer Research and Modeling, 7:2 (2015), 243–251
Linking options:
https://www.mathnet.ru/eng/crm183 https://www.mathnet.ru/eng/crm/v7/i2/p243
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Abstract page: | 135 | Full-text PDF : | 221 | References: | 25 |
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