A new model to optimize the architecture of a fault-tolerant modular neurocomputer

In this paper, we present some results on error detection and correction in a modular neurocomputer that are based on redundant residue number systems. The error correction method developed below involves the modified Chinese Remainder Theorem with fractions and uses a Hopfield neural network to cor...

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Hlavní autoři:	Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Babenko, M. G., Бабенко, М. Г., Lavrinenko, I. N., Лавриненко, И. Н., Lavrinenko, A. V., Лавриненко, А. В., Deryabin, M. A., Дерябин, М. А., Nazarov, A. S., Назаров, А. С.
Médium:	Статья
Jazyk:	English
Vydáno:	Elsevier B.V. 2018
Témata:	Digital neuron Hopfield neural network Modular neurocomputer Residue number system (RNS) Very large scale integrated (VLSI) circuit
On-line přístup:	https://www.scopus.com/record/display.uri?eid=2-s2.0-85046691005&origin=resultslist&sort=plf-f&src=s&nlo=1&nlr=20&nls=afprfnm-t&affilName=nort+caucas+fed+univ&sid=66c0b1a32aef918b6dc8f22f4ab4e9b7&sot=afnl&sdt=afsp&sl=53&s=%28AF-ID%28%22North+Caucasus+Federal+University%22+60070541%29%29&relpos=0&citeCnt=0&searchTerm= https://dspace.ncfu.ru:443/handle/20.500.12258/220
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A new model to optimize the architecture of a fault-tolerant modular neurocomputer

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