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Cybernetics and Systems Analysis

, Volume 53, Issue 4, pp 627–635 | Cite as

Synthesis of Neural-Like Networks on the Basis of Conversion of Cyclic Hamming Codes

NEW TOOLS OF CYBERNETICS, INFORMATICS, COMPUTER ENGINEERING, AND SYSTEMS ANALYSIS
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Abstract

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.

Keywords

Boolean function neural-like network Hamming distance cyclic code 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.V. M. Glushkov Institute of CyberneticsNational Academy of Sciences of UkraineKyivUkraine
  2. 2.Taras Shevchenko National University of KyivKyivUkraine

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