@article{id:Siminski2022Prototype,
	title = {Prototype based granular neuro-fuzzy system for regression task},
	journal = {Fuzzy Sets and Systems},
	volume = {449},
	pages = {56-78},
	year = {2022}, 
	issn = {0165-0114},
	doi = {10.1016/j.fss.2022.03.001},
	author = {Krzysztof Siminski},
	url = {https://www.sciencedirect.com/science/article/pii/S0165011422000963},
	keywords = {Prototype, Granular computing, Neuro-fuzzy system, Regression},
	abstract = "Artificial intelligence is often inspired by biological solutions. 
	Prototypes are among these inspirations. Humans often describe complex
	entities by comparing them to previously known items (prototypes) 
	instead of providing a detailed description. In this study, we apply 
	this approach to neuro-fuzzy systems. Neuro-fuzzy systems operate using 
	fuzzy IF-THEN rules. In our approach, the premises of rules are represented 
	by prototypes. A new item is compared to the prototypes (as wholes) 
	in the rule premises and its similarities to the prototypes in the rules 
	define the firing strengths of the rules. The similarity is assessed based 
	on the Minkowski metric. Prototypes are elaborated by granulation 
	of the input domain with clustering and by applying the principle 
	of justifiable granularity. We tested the proposed method in numerical 
	experiments based on the regression task for benchmark data sets 
	in public data repositories.",
}