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Neuron dynamical models and optimal prediction

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dc.contributor.author BOGHANASTJUK, Victoria
dc.contributor.author KOBILEATZKY, Nicolae
dc.contributor.author MARIAN, Călin
dc.contributor.author BERDAGA, Olesea
dc.contributor.author TONU, Alexei
dc.contributor.author LARCHENCOV, Alexandru
dc.contributor.author ALEXEIENKO, Eugen
dc.contributor.author ERHAN, Liliana
dc.date.accessioned 2019-10-29T08:54:07Z
dc.date.available 2019-10-29T08:54:07Z
dc.date.issued 2005
dc.identifier.citation BOGHANASTJUK, Victoria, KOBILEATZKY, Nicolae, MARIAN, Călin et al. Neuron dynamical models and optimal prediction. In: Microelectronics and Computer Science: proc. of the 4th intern. conf., September 15-17, 2005. Chişinău, 2005, vol. 2, pp. 99-102. ISBN 9975-66-038-X. en_US
dc.identifier.isbn 9975-66-038-X
dc.identifier.uri http://repository.utm.md/handle/5014/5542
dc.description.abstract This paper describes a new approach to informational technological system the mathematical Hopfield model is derived for the dynamic terminal expansion of the content addressable memory in dynamic systems. The model structure has been specifically designed to facilitate control studies. This a real – time temporal supervised learning algorithm leads to a system. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject neuron models en_US
dc.subject additive models en_US
dc.subject Hopfield models en_US
dc.subject prediction en_US
dc.subject networks en_US
dc.title Neuron dynamical models and optimal prediction en_US
dc.type Article en_US


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