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.
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