Ivan Peleshchak

Work place: Department of Information Systems and Networks, Lviv Polytechnic National University, Lviv, 79012, Ukraine

E-mail: peleshchakivan@gmail.com


Research Interests: Data Structures and Algorithms, Information Systems, Computer systems and computational processes


Ivan Peleshchak is an assistant of the Information Systems and Networks Department in Lviv Polytechnic National University, PhD.

Author Articles
Data Clustering by Chaotic Oscillatory Neural Networks with Dipole Synaptic Connections

By Roman Peleshchak Vasyl Lytvyn Ivan Peleshchak Dmytro Dudyk Dmytro Uhryn

DOI: https://doi.org/10.5815/ijmecs.2024.03.03, Pub. Date: 8 Jun. 2024

This article introduces a novel approach to data clustering based on the oscillatory chaotic neural network with dipole synaptic connections. The conducted research affirms that the proposed model effectively facilitates the formation of clusters of objects with similar properties due to the use of a slowly decreasing function of the dipole synaptic strength. The studies demonstrate that the degree of neuron synchronization in networks with dipole synaptic connections surpasses that in networks with Gaussian synaptic connections. The findings also indicate an increase in the interval of the resolution range in the model featuring dipole neurons, underscoring the effectiveness of the proposed method.

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Structural Transformations of Incoming Signal by a Single Nonlinear Oscillatory Neuron or by an Artificial Nonlinear Neural Network

By Roman Peleshchak Vasyl Lytvyn Oksana Bihun Ivan Peleshchak

DOI: https://doi.org/10.5815/ijisa.2019.08.01, Pub. Date: 8 Aug. 2019

Structural transformations of incoming informational signal by a single nonlinear oscillatory neuron or an artificial nonlinear neural network are investigated. The neurons are modeled as threshold devices so that the artificial nonlinear neural network under consideration are systems of nonlinear van der Pol type oscillatory neurons. The neurons are coupled by synaptic weight coefficients to endow the systems with the configuration topology of a chain or a ring. It is shown that the morphology of the outgoing signal – with respect to the shape, amplitude and time dependence of the instantaneous frequency of the signal – at the output of such a neural network has a higher degree of stochasticity than the morphology of the signal at the output of a single neuron. We conclude that the process of coding by a single neuron or an entire chain-like or circular neural network may be considered in terms of frequency modulations, which are known in Physics as a way to transmit information. We conjecture that frequency modulations constitute one of the ways of coding of information by the neurons in these types of neural networks.

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Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model

By Vasyl Lytvyn Victoria Vysotska Ivan Peleshchak Ihor Rishnyak Roman Peleshchak

DOI: https://doi.org/10.5815/ijisa.2018.04.02, Pub. Date: 8 Apr. 2018

Time-frequency and time dependence of the output signal morphology of nonlinear oscillator neuron based on Van der Pol model using analytical and numerical methods were investigated. Threshold effect neuron, when it is exposed to external non-stationary signals that vary in shape, frequency and amplitude was considered.

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