Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior

Shumixin A.G., Boyarshinova A.S.
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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
Citation in English: Shumixin A.G., Boyarshinova A.S. Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior // Computer Research and Modeling, 2015, vol. 7, no. 2, pp. 243-251
Citation in English: Shumixin A.G., Boyarshinova A.S. Algorithm of artificial neural network architecture and training set size configuration within approximation of dynamic object behavior // Computer Research and Modeling, 2015, vol. 7, no. 2, pp. 243-251
DOI: 10.20537/2076-7633-2015-7-2-243-251
URL: http://crm-en.ics.org.ru/journal/article/2260/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.
According to Crossref, this article is cited by:
  • Aleksandr Georgievich Shumixin, Anna Sergeevna Aleksandrova. Identification of a controlled object using frequency responses obtained from a dynamic neural network model of a control system. // Computer Research and Modeling. — 2017. — V. 9, no. 5. — P. 729. DOI: 10.20537/2076-7633-2017-9-5-729-740
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