Olena O. Boiko

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

E-mail: olena.boiko@ukr.net


Research Interests: Computer systems and computational processes, Systems Architecture


Olena Boiko graduated from Kharkiv National University of Radio Electronics in 2011. She is a PhD student in Computer Science at Kharkiv National University of Radio Electronics. Her current interests are Time Series Forecasting, Fuzzy Clustering, Evolving Neuro-Fuzzy Systems.

Author Articles
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: https://doi.org/10.5815/ijitcs.2016.10.01, 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: https://doi.org/10.5815/ijisa.2016.09.01, 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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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: https://doi.org/10.5815/ijmecs.2016.05.02, 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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Other Articles