Yevgeniy V. Bodyanskiy

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



Research Interests: Control Theory, Data Structures and Algorithms, Systems Architecture, Real-Time Computing, Computer systems and computational processes, Computational Science and Engineering


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 in 1990. He obtained an academic title of the Professor in 1994.

Prof. Bodyanskiy has been the professor of Artificial Intelligence Department at KhNURE, the Head of Control Systems Research Laboratory at KhNURE. He has more than 500 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
An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

By Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Daria S. Kopaliani

DOI:, Pub. Date: 8 Jan. 2015

A modification of the neo-fuzzy neuron is proposed (an extended neo-fuzzy neuron (ENFN)) that is characterized by improved approximating properties. An adaptive learning algorithm is proposed that has both tracking and smoothing properties and solves prediction, filtering and smoothing tasks of non-stationary “noisy” stochastic and chaotic signals. An ENFN distinctive feature is its computational simplicity compared to other artificial neural networks and neuro-fuzzy systems.

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A Multidimensional Cascade Neuro-Fuzzy System with Neuron Pool Optimization in Each Cascade

By Yevgeniy V. Bodyanskiy Oleksii K. Tyshchenko Daria S. Kopaliani

DOI:, Pub. Date: 8 Jul. 2014

A new architecture and learning algorithms for the multidimensional hybrid cascade neural network with neuron pool optimization in each cascade are proposed in this paper. The proposed system differs from the well-known cascade systems in its capability to process multidimensional time series in an online mode, which makes it possible to process non-stationary stochastic and chaotic signals with the required accuracy. Compared to conventional analogs, the proposed system provides computational simplicity and possesses both tracking and filtering capabilities.

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Other Articles