 Cybernetics and Systems Analysis /  Issue (2017, 53 (4))
Opanasenko V.N.,
Kryvyi S.L.
Synthesis of neural-like networks based on the conversion of cyclic Hamming codes This article considers the synthesis of a neural-like Hamming network with a view to implementing the problem of classification of an input set of binary vectors. The formation of a sequence sorted by the Hamming distance as the proximity measure is based on the conversion of cyclic Hamming codes. The correctness of the synthesis of such an implementation for an arbitrary Hamming distance and a binary input vector of arbitrary length is proved. © 2017, Springer Science+Business Media, LLC. Keywords: Boolean function, cyclic code, Hamming distance, neural-like network, Bins, Block codes, Boolean functions, Codes (symbols), Network coding, Binary inputs, Binary vectors, Cyclic code, Hamming code, Hamming networks, Input set, Neural-like network, Proximity measure, Hamming distance
Cite:
Opanasenko V.N.,
Kryvyi S.L.
(2017). Synthesis of neural-like networks based on the conversion of cyclic Hamming codes. Cybernetics and Systems Analysis, 53 (4), 155-164. doi: https://doi.org/10.1007/s10559-017-9965-z http://jnas.nbuv.gov.ua/article/UJRN-0000719130 [In Russian]. |
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