@article{id:Siminski2021GrNFS,
	author = "Krzysztof Siminski",
	doi = "10.34768/amcs-2021-0030",
	journal = "International Journal of Applied Mathematics and Computer Science",
	number = "3",
	pages = "445–459",
	title = "{GrNFS} – Granular neuro-fuzzy system for regression in large volume data",
	volume = "31",
	year = "2021",
	abstract = "Neuro-fuzzy systems have proved their ability to elaborate intelligible 
	nonlinear models for presented data. However, their bottleneck is the volume of data. 
	They have to read all data in order to produce a model. We apply the granular approach 
	and propose a granular neuro-fuzzy system for large volume data. In our method the data 
	are read by parts and granulated. In the next stage the fuzzy model is produced not 
	on data but on granules. In the paper we introduce a novel type of granules: a fuzzy
	rule. In our system granules are represented by both regular data items and fuzzy rules.
	Fuzzy rules are a kind of data summaries. The experiments show that the proposed 
	granular neuro-fuzzy system can produce intelligible models even for large volume 
	datasets. The system outperforms the sampling techniques for large volume datasets."
}