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dc.contributor.authorNazarov, P. V.-
dc.contributor.authorPopleteev, A. M.-
dc.contributor.authorLutkovski, V. M.-
dc.contributor.authorApanasovich, V. V.-
dc.date.accessioned2013-11-05T07:34:32Z-
dc.date.available2013-11-05T07:34:32Z-
dc.date.issued2004-
dc.identifier.urihttp://elib.bsu.by/handle/123456789/50782-
dc.description.abstractArtificial neural networks (ANNs) are widely used as "black-box" models of complex processes and systems. Although the neural network simulation of deterministic processes is a well-known area, ANN simulation of stochastic ones is still at the frontier of the ANN methodology. In the current work, the method of ANN simulation of д-correlated stochastic signals is proposed. The main advantage of the scheme is the utilization of a standard multilayer perceptron instead of complex stochastic ANN structures. The network receives input parameters of simulation together with basic random values and generates the desired stochastic signal.ru
dc.language.isoenru
dc.publisherМинск: БГУru
dc.subjectЭБ БГУ::ОБЩЕСТВЕННЫЕ НАУКИ::Информатикаru
dc.titleNeural Network Simulation Of Δ-Correlated Stochastic Signalsru
dc.typeArticleru
Appears in Collections:2004. Международная конференция “Моделирование процессов и систем”

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