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dc.contributor.authorChervyakov, N. I.-
dc.contributor.authorЧервяков, Н. И.-
dc.contributor.authorLyakhov, P. A.-
dc.contributor.authorЛяхов, П. А.-
dc.contributor.authorBabenko, M. G.-
dc.contributor.authorБабенко, М. Г.-
dc.contributor.authorGaryanina, A. I.-
dc.contributor.authorГарянина, А. И.-
dc.contributor.authorLavrinenko, I. N.-
dc.contributor.authorЛавриненко, И. Н.-
dc.contributor.authorLavrinenko, A. V.-
dc.contributor.authorЛавриненко, А. В.-
dc.contributor.authorDeryabin, M. A.-
dc.contributor.authorДерябин, М. А.-
dc.identifier.citationChervyakov, N.I., Lyakhov, P.A., Babenko, M.G., Garyanina, A.I., Lavrinenko, I.N., Lavrinenko, A.V., Deryabin, M.A. An efficient method of error correction in fault-tolerant modular neurocomputers // Neurocomputing. - 2016. - Volume 205. - Pages 32-44ru
dc.description.abstractIn this paper, we propose the architecture of a fault-tolerant unit in a modular neurocomputer that is based on decoding with computation of errors syndromes on redundant moduli and implemented using FPGA and a finite ring neural network. The computational complexity of the proposed architecture is about 80% less in comparison with that of the architecture based on number projections in the mixed radix number systemru
dc.subjectArtificial neural networkru
dc.subjectChinese remainder theorem (CRT)ru
dc.subjectMixed radix conversionru
dc.subjectResidue number system (RNS)ru
dc.subjectNetwork architectureru
dc.titleAn efficient method of error correction in fault-tolerant modular neurocomputersru
vkr.amountPages 32-44ru
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