Approximation of complex, multiparameter, essentially nonlinear, dynamic relationships based on genetic algorithms

Please use this identifier to cite or link to this item: https://elib.psu.by/handle/123456789/22789

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DC Field	Value	Language
dc.contributor.author	Glukhov, A.	-
dc.contributor.author	Glukhov, D.	-
dc.contributor.author	Trofimov, V.	-
dc.contributor.author	Trofimova, L.	-
dc.date.accessioned	2018-10-24T08:17:34Z	-
dc.date.available	2018-10-24T08:17:34Z	-
dc.date.issued	2016	-
dc.identifier.citation	Glukhov, A. O. Approximation of complex, multiparameter, essentially nonlinear, dynamic relationships based on genetic algorithms / A. O. Glukhov, D. O. Glukhov, V. V. Trofimov, L. A. Trofimova // Proceedings of the XIX International Conference on Soft Computing and Measurements SCM`2016. St. Petersburg: IEEE, 2016. pp. 252-254, doi: 10.1109/SCM.2016.7519744	ru_RU
dc.identifier.uri	https://elib.psu.by/handle/123456789/22789	-
dc.description.abstract	The work is focused on usage of genetic algorithms to get more precise approximation of complex and essentially nonlinear dynamic relationships. The algorithms precision was measured based on the model of stock market index prediction.	ru_RU
dc.language.iso	en	ru_RU
dc.publisher	IEEE	-
dc.subject	Genetic algorithms	ru_RU
dc.subject	Complex	ru_RU
dc.subject	Multiparameter	ru_RU
dc.subject	Essentially nonlinear	ru_RU
dc.subject	Dynamic dependencies	ru_RU
dc.subject	Stock exchange index	ru_RU
dc.title	Approximation of complex, multiparameter, essentially nonlinear, dynamic relationships based on genetic algorithms	ru_RU
dc.type	Article	ru_RU
dc.identifier.doi	10.1109/SCM.2016.7519744	-
Appears in Collections:	Публикации в Scopus и Web of Science

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