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Information Technologies and Telecommunications
Methods of effective data analysis
for solving poorly formalized recognition tasks
L. A. Lyutikova Institute of Applied Mathematics and Automation –
branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences,
360000, Russia, Nalchik, 89 A Shortanov street
Abstract:
The paper presents a comparative analysis of the results of neural network solutions and
logical algorithms. A method is proposed to improve the results of neural network solutions. The work
considers a sigma-pi neural network as a solver, and the data are represented by objects and features
characterizing these objects. The paper shows that there are logical approaches that can most fully describe
all possible patterns in the study area. At the same time, neural networks provide only a part of the possible
solutions as solutions. Therefore, it is proposed to analyze and supplement these solutions. To do this, the
paper considers the possibility of constructing a logical corrector capable of constructing a logical function
according to the structure of a neural network, and then implementing it in the form of logical neural
networks. This approach allows to identify logical relationships between objects in the data under study,
knowledge of logical patterns will allow to formalize and more accurately understand the nature of the
analyzed area.
Keywords:
logical classifier, neural network, $\Sigma\Pi$-neuron, decision rules.
Received: 16.11.2022 Revised: 30.11.2022 Accepted: 01.12.2022
Citation:
L. A. Lyutikova, “Methods of effective data analysis
for solving poorly formalized recognition tasks”, News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2022, no. 6, 106–114
Linking options:
https://www.mathnet.ru/eng/izkab518 https://www.mathnet.ru/eng/izkab/y2022/i6/p106
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Statistics & downloads: |
Abstract page: | 29 | Full-text PDF : | 277 | References: | 6 |
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