Synthesis of Neural-Like Networks on the Basis of Conversion of Cyclic Hamming Codes
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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.
KeywordsBoolean function neural-like network Hamming distance cyclic code
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