Analytical and Numerical Study of Information Retrieval Method Based on Single-Layer Neural Network with Optimization of Computing Algorithm Performance

Konstantin Kostromitin (), Konstantin Melnikov and Dar’ya Nikonova
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Konstantin Kostromitin: Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia
Konstantin Melnikov: Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia
Dar’ya Nikonova: Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia

Mathematics, 2023, vol. 11, issue 17, 1-20

Abstract: This work presents a mathematical model of a fast-acting single-layer artificial neural network applied to the task of image reconstruction after noise. For research purposes, this algorithm was implemented in the Python and C++ programming languages. The numerical simulation of the recovery efficiency of the described neural network was performed for different values of the noise factor, the number of samples required to train elements in the sample and the dimensionality of the coupling coefficients, w . The study of the mathematical model of this neural network is presented; as a result, it is possible to identify its essence, to reduce the number of operations required to recover a single element and to increase recovery accuracy by changing the order of calculation of coupling coefficients, w .

Keywords: single-layer neural network model; data recovery; noise; numerical simulation; coupling coefficients (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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