Please use this identifier to cite or link to this item: https://elib.psu.by/handle/123456789/416
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dc.contributor.authorKapelko, V.-
dc.contributor.authorLinkevich, A.-
dc.date.accessioned2013-10-17T06:32:53Z-
dc.date.available2013-10-17T06:32:53Z-
dc.date.issued2011-
dc.identifier.citationВестник Полоцкого государственного университета. Серия C, Фундаментальные науки: научно-теоретический журнал.- Новополоцк : ПГУ, 2011.- № 12.- С. 16-18.ru_RU
dc.identifier.urihttps://elib.psu.by/handle/123456789/416-
dc.description.abstractWe study probabilistic synchronous dynamics of Little-Hopfield neural networks with asymmetric interneuronal synaptic connections adjusted in accordance with a learning rule given in [3]. Types of behaviour of such systems are analysed in dependence of a vector-parameter characterizing degrees of freedom in determining synaptic couplings. We have found that in the case of small level of noise networks can store memorized patterns but as an amount of noise is increased behaviour becomes more complex and, beginning with some critical value the motion seems to be chaotic.en_EN
dc.language.isoenru_RU
dc.publisherПолоцкий государственный университетru_RU
dc.relation.ispartofВеснік Полацкага дзяржаўнага ўніверсітэта. Серыя C, Фундаментальныя навукіbe_BE
dc.relation.ispartofHerald of Polotsk State University. Series C, Fundamental sciencesen_EN
dc.relation.ispartofВестник Полоцкого государственного университета. Серия C, Фундаментальные наукиru_RU
dc.relation.ispartofseriesСерия C, Фундаментальные науки;2011. - № 12-
dc.rightsopen accessru_RU
dc.subjectМатематическая кибернетикаru_RU
dc.subjectнейронные сетиru_RU
dc.subjectдинамические моделиru_RU
dc.titleStochastic dynamics of neural networks after learningen_EN
dc.typeArticleru_RU
Appears in Collections:2011, № 12

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