Data Clustering by Chaotic Oscillatory Neural Networks with Dipole Synaptic Connections

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Roman Peleshchak 1 Vasyl Lytvyn 1 Ivan Peleshchak 1 Dmytro Dudyk 1 Dmytro Uhryn 2,*

1. Lviv Polytechnic National University, Lviv, 79013, Ukraine

2. Yuriy Fedkovych Chernivtsi National University, Chernivtsi, 58012, Ukraine

* Corresponding author.


Received: 21 Jan. 2024 / Revised: 10 Feb. 2024 / Accepted: 16 Mar. 2024 / Published: 8 Jun. 2024

Index Terms

Data Clustering, Chaotic Oscillatory Neural Network, Dipole Synaptic Connections


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.

Cite This Paper

Roman Peleshchak, Vasyl Lytvyn, Ivan Peleshchak, Dmytro Dudyk, Dmytro Uhryn, "Data Clustering by Chaotic Oscillatory Neural Networks with Dipole Synaptic Connections", International Journal of Modern Education and Computer Science(IJMECS), Vol.16, No.3, pp. 27-38, 2024. DOI:10.5815/ijmecs.2024.03.03


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