Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/18054
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dc.contributor.authorVershkov, N. A.-
dc.contributor.authorВершков, Н. А.-
dc.contributor.authorKuchukov, V. A.-
dc.contributor.authorКучуков, В. А.-
dc.contributor.authorKuchukova, N. N.-
dc.contributor.authorКучукова, Н. Н.-
dc.contributor.authorKucherov, N. N.-
dc.contributor.authorКучеров, Н. Н.-
dc.contributor.authorShiriaev, E. M.-
dc.contributor.authorШиряев, Е. М.-
dc.date.accessioned2021-09-01T09:18:32Z-
dc.date.available2021-09-01T09:18:32Z-
dc.date.issued2021-
dc.identifier.citationVershkov N. А., Kuchukov V. A., Kuchukova N. N., Kucherov N. N., Shiriaev E. M. Optimization of computational complexity of an artificial neural network // CEUR Workshop Proceedings. - 2021. - Том 2913. - Стр. 220 - 226ru
dc.identifier.urihttp://hdl.handle.net/20.500.12258/18054-
dc.description.abstractThe article deals with the modelling of Artificial Neural Networks as an information transmission system to optimize their computational complexity. The analysis of existing theoretical approaches to optimizing the structure and training of neural networks is carried out. In the process of constructing the model, the well-known problem of isolating a deterministic signal on the background of noise and adapting it to solving the problem of assigning an input implementation to a certain cluster is considered. A layer of neurons is considered as an information transformer with a kernel for solving a certain class of problems: orthogonal transformation, matched filtering, and nonlinear transformation for recognizing the input implementation with a given accuracy. Based on the analysis of the proposed model, it is concluded that it is possible to reduce the number of neurons in the layers of neural network and to reduce the number of features for training the classifierru
dc.language.isoenru
dc.publisherCEUR-WSru
dc.relation.ispartofseriesCEUR Workshop Proceedings-
dc.subjectMathematical transformationsru
dc.subjectNetwork layersru
dc.subjectComplex networksru
dc.subjectComputational complexityru
dc.subjectControl systemsru
dc.subjectInformation filteringru
dc.subjectMultilayer neural networksru
dc.titleOptimization of computational complexity of an artificial neural networkru
dc.typeСтатьяru
vkr.instИнститут математики и информационных технологий имени профессора Н.И. Червяковаru
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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