Daria S. Kopaliani

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

E-mail: daria.kopaliani@gmail.com


Research Interests: Engineering


Daria Kopaliani 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, Evolving and Cascade Neuro-Fuzzy Systems.

Author Articles
An Extended Neo-Fuzzy Neuron and its Adaptive Learning Algorithm

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

DOI: https://doi.org/10.5815/ijisa.2015.02.03, 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: https://doi.org/10.5815/ijitcs.2014.08.02, 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