Neuron dynamical models and optimal prediction

BOGHANASTJUK, Victoria; KOBILEATZKY, Nicolae; MARIAN, Călin; BERDAGA, Olesea; TONU, Alexei; LARCHENCOV, Alexandru; ALEXEIENKO, Eugen; ERHAN, Liliana

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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