Yevgeniy V. Bodyanskiy

Work place: Artificial Intelligence Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine



Research Interests: Big data and learning analytics, Artificial Intelligence, Neural Networks, Pattern Recognition, Data Mining, Data Structures and Algorithms


Yevgeniy V. Bodyanskiy

Professor at the Department of Artificial Intelligence, Scientific Head at the CSRL, Member of the specialized scientific council, Member of STC Presidium, IEEE Senior Member, Doctor of Technical Sciences.






Author Articles
An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Olena O. Boiko

DOI:, Pub. Date: 8 May 2016

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders’ blurriness. Clusters’ quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.

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An Evolving Neuro-Fuzzy System with Online Learning/Self-learning

By Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Anastasiia O. Deinekob

DOI:, Pub. Date: 8 Feb. 2015

A new neuro-fuzzy system’s architecture and a learning method that adjusts its weights as well as automatically determines a number of neurons, centers’ location of membership functions and the receptive field’s parameters in an online mode with high processing speed is proposed in this paper. The basic idea of this approach is to tune both synaptic weights and membership functions with the help of the supervised learning and self-learning paradigms. The approach to solving the problem has to do with evolving online neuro-fuzzy systems that can process data under uncertainty conditions. The results proves the effectiveness of the developed architecture and the learning procedure.

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Other Articles