@article{bodyanskiy_evolving_2017,
	series = {Online {Real}-{Time} {Learning} {Strategies} for {Data} {Streams}},
	title = {An evolving connectionist system for data stream fuzzy clustering and its online learning},
	volume = {262},
	issn = {0925-2312},
	url = {https://www.sciencedirect.com/science/article/pii/S0925231217309785},
	doi = {10.1016/j.neucom.2017.03.081},
	abstract = {An evolving cascade neuro-fuzzy system and its online learning procedure are considered in this paper. The system is based on conventional Kohonen neurons. The proposed system solves a clustering task of non-stationary data streams under uncertainty conditions when data come in the form of a sequential stream in an online mode. A quality estimation process is defined by finding an optimal value of the used cluster validity index.},
	language = {en},
	urldate = {2021-10-01},
	journal = {Neurocomputing},
	author = {Bodyanskiy, Yevgeniy V. and Tyshchenko, Oleksii K. and Kopaliani, Daria S.},
	month = nov,
	year = {2017},
	keywords = {Data stream, Evolving connectionist system, Fuzzy clustering, Neuro-fuzzy network},
	pages = {41--56},
}

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