Yevgeniy V. Bodyanskiy

Work place: Kharkiv National University of Radio Electronics, Kharkiv, Ukraine



Research Interests: Computational Science and Engineering, Computer systems and computational processes, Computer Architecture and Organization, Systems Architecture, Control Theory


Yevgeniy Bodyanskiy. graduated from Kharkiv National University of Radio Electronics in 1971. He got his PhD in 1980. He obtained an academic title of the Senior Researcher in 1984. He got his Dr.Habil.Sci.Eng. in 1990. He obtained an academic title of the Professor in 1994.

Prof. Bodyanskiy is Professor of Artificial Intelligence Department at KhNURE, Head of Control Systems Research Laboratory at KhNURE. He has more than 600 scientific publications including 40 inventions and 10 monographs. His research interests are Hybrid Systems of Computational Intelligence: adaptive, neuro-, wavelet-, neo-fuzzy-, real-time systems that have to do with control, identification, and forecasting, clustering, diagnostics and fault detection.

Prof. Bodyanskiy is an IEEE Senior Member and a member of 4 scientific and 7 editorial boards.

Author Articles
A Multidimensional Extended Neo-Fuzzy Neuron for Facial Expression Recognition

By Zhengbing Hu Yevgeniy V. Bodyanskiy Nonna Ye. Kulishova Oleksii K. Tyshchenko

DOI:, Pub. Date: 8 Sep. 2017

An article introduces a modified architecture of the neo-fuzzy neuron, also known as a "multidimensional extended neo-fuzzy neuron" (MENFN), for the face recognition problems. This architecture is marked by enhanced approximating capabilities. A characteristic property of the MENFN is also its computational plainness in comparison with neuro-fuzzy systems and neural networks. These qualities of the proposed system make it effectual for solving the image recognition problems. An introduced MENFN’s adaptive learning algorithm allows solving classification problems in a real-time fashion.

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Fuzzy Clustering Data Arrays with Omitted Observations

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Vitalii M. Tkachov

DOI:, Pub. Date: 8 Jun. 2017

An adaptive neural system which solves a problem of clustering data with missing values in an online mode with a permanent correction of restorable table elements and clusters’ centroids is proposed in this article. The introduced neural system is characterized by both a high speed and a simple numerical implementation. It can process information in a real-time mode.

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Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI:, Pub. Date: 8 May 2017

Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.

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Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI:, Pub. Date: 8 Feb. 2017

A task of clustering data given on the ordinal scale under conditions of overlapping clusters has been considered. It’s proposed to use an approach based on membership and likelihood functions sharing. A number of performed experiments proved effectiveness of the proposed method. The proposed method is characterized by robustness to outliers due to a way of ordering values while constructing membership functions.

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Fuzzy Clustering Data Given in the Ordinal Scale

By Zhengbing Hu Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Viktoriia O. Samitova

DOI:, Pub. Date: 8 Jan. 2017

A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.

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Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model

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

DOI:, Pub. Date: 8 Oct. 2016

An evolving weighted neuro-neo-fuzzy-ANARX model and its learning procedures are introduced in the article. This system is basically used for time series forecasting. It's based on neo-fuzzy elements. This system may be considered as a pool of elements that process data in a parallel manner. The proposed evolving system may provide online processing data streams.

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An Evolving Cascade System Based on a Set of Neo - Fuzzy Nodes

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

DOI:, Pub. Date: 8 Sep. 2016

Neo-fuzzy elements are used as nodes for an evolving cascade system. The proposed system can tune both its parameters and architecture in an online mode. It can be used for solving a wide range of Data Mining tasks (namely time series forecasting). The evolving cascade system with neo-fuzzy nodes can process rather large data sets with high speed and effectiveness.

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Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis

By Yevgeniy V. Bodyanskiy Olena Vynokurova Volodymyr Savvo Tatiana Tverdokhlib Pavlo Mulesa

DOI:, Pub. Date: 8 Aug. 2016

In the paper, the hybrid clustering-classification neural network is proposed. This network allows to increase a quality of information processing under the condition of overlapping classes due to the rational choice of learning rate parameter and introducing special procedure of fuzzy reasoning in the clustering-classification process, which occurs both with external learning signal (“supervised”), and without one (“unsupervised”). As similarity measure neighborhood function or membership one, cosine structures are used, which allow to provide a high flexibility due to self-learning-learning process and to provide some new useful properties. Many realized experiments have confirmed the efficiency of proposed hybrid clustering-classification neural network; also, this network was used for solving diagnostics task of reactive arthritis.

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