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...

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Bibliographic Details
Published in:Journal of mathematical sciences (New York, N.Y.) N.Y.), 2021-03, Vol.253 (4), p.539-546
Main Author: Lyutikova, L. A.
Format: Article
Language:English
Subjects:
Algebra
Algorithms
Construction
Data analysis
Mathematics
Mathematics and Statistics
Neural networks
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Summary: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.
ISSN:1072-3374
1573-8795
DOI:10.1007/s10958-021-05251-3