Construction of a Logical-Algebraic Corrector to Increase the Adaptive Properties of the ΣΠ-Neuron


In this paper, we consider the problem of constructing a correction algorithm with the aim of increasing the adaptive properties of the ΣΠ-neuron, relying solely on the structure of the ΣΠ-neuron itself. To build the corrector, the logical-algebraic method of data analysis is used. Comparison of the advantages of the neural network approach and the logical-algebraic method suggests that a combined approach to the organization of the neural network improves its efficiency and allows one to build a set of rules that reveal hidden patterns in a given subject area, thus improving the quality of the recognition system.

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Correspondence to L. A. Lyutikova.

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Translated from Itogi Nauki i Tekhniki, Seriya Sovremennaya Matematika i Ee Prilozheniya. Tematicheskie Obzory, Vol. 154, Proceedings of the International Conference “Actual Problems of Applied Mathematics and Physics,” Kabardino-Balkaria, Nalchik, May 17–21, 2017, 2018.

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Lyutikova, L.A. Construction of a Logical-Algebraic Corrector to Increase the Adaptive Properties of the ΣΠ-Neuron. J Math Sci 253, 539–546 (2021).

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Keywords and phrases

  • ΣΠ-neuron
  • algorithm
  • corrector
  • classifier
  • predicate
  • disjunctive normal form
  • logical function

AMS Subject Classification

  • 68T05
  • 68T27