Work place: Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
E-mail: daria.kopaliani@gmail.com
Website:
Research Interests: Engineering
Biography
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.
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.
[...] Read more.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.
[...] Read more.Subscribe to receive issue release notifications and newsletters from MECS Press journals