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Neural net decoders of nonbinary codes

Journal of physics. Conference series, 2020-03, Vol.1479 (1), p.12089 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1479/1/012089

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  • Title:
    Neural net decoders of nonbinary codes
  • Author: Butov, V V ; Dumachev, V N ; Fedyaeva, S E
  • Subjects: Classifiers ; Decoders ; Error analysis ; Error correction ; Physics ; Symbols
  • Is Part Of: Journal of physics. Conference series, 2020-03, Vol.1479 (1), p.12089
  • Description: In paper, neural net decoders of nonbinary error correction codes are considered. Analytic methods for calculating of synapse weight coefficients are proposed. It is shown that for codes (n, k) with a small number of corrected errors (n − k ≪ k), it is advisable to use a 6-layer universal classifier based on the feature space of parity symbols. For codes with k ≪ n − k, it is proposed to use a 3-layer classifier on feature space of information symbols.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1479/1/012089
  • Source: Open Access: IOP Publishing Free Content
    Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    ProQuest Central
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