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Discrete perceptron based on probabilistic estimates of shifted synaptic signals

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Category: Content №2 2025

Last Updated on 26 April 2025

Published on 30 November -0001

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Authors:

S.V.Yakovyn, orcid.org/0000-0002-3335-2892, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.I.Melnychuk*, orcid.org/0000-0002-6973-4235, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

повний текст / full article

Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (2): 189 - 196

https://doi.org/10.33271/nvngu/2025-2/189

Abstract:

Purpose. Improvement of existing methods and development of new approaches for the digital processing of discrete signals in the field of artificial neural networks, as well as the formulation of principles for the structural implementation of discrete perceptrons.

Methodology. The presented scientific results and conclusions were obtained using methods of statistical analysis and digital signal processing, probability theory and mathematical statistics, signal and system theory, as well as through computational experiments and modeling.

Findings. The research showed the potential and prospects of using the proposed perceptron structure, which processes synaptic signals based on statistical estimation of their discrete states. The signal shifting is implemented through addition of weight coefficients, which allows avoiding multiplication operations and, as a result, reduces algorithmic and computational complexity. Furthermore, using a discrete basis allows accelerating the training process by reducing the possible range of signal values.

Originality. A method for implementing a discrete perceptron that is based on the use of statistical evaluations and integer operations on discrete synaptic signals has been proposed for the first time.

Practical value. The proposed approach allows avoiding the typical multiplication operation for perceptron structures, reducing computational costs. The use of a discrete basis significantly limits the value space of shift coefficients which could potentially shorten the training process. An important aspect of the results obtained is the prospects for implementing specialised artificial neural networks on platforms with limited computing resources, such as microcontrollers, programmable logic integrated circuits, and others.

Keywords: perceptron, statistical estimates, discrete signals, signal processing

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