@string{ijamcs="International Journal of Applied Mathematics and Computer Science"}
@article{id:Siminski2012NeuroRoughFuzzy,
    title = {Neuro-rough-fuzzy approach for regression modelling from missing data},
    author = {Krzysztof Simi\'{n}ski},
    affiliation = {Silesian University of Technology Institute of Informatics Akademicka 16, 44-100 Gliwice, Poland},
    journal = IJAMCS,
    year = 2012,
    pages = {461--476},
    volume = 22, 
    number = 2,
    doi={10.2478/v10006-012-0035-4},
    note = {[20 pkt; impact factor: 0.794]},
    abstract = {Real life data sets often suffer from missing data. The hitherto proposed
	neuro-rough-fuzzy systems often cannot handle such  situations. The
	paper presents the neuro-fuzzy system for data sets with missing
	values. The proposed solution is the complete neuro-fuzzy systems.
	The system creates the rough fuzzy model from presented data (both
	full and with missing values) and is able to elaborate the answer
	for full and missing data examples. The paper also describes the
	dedicated clustering algorithm.  The paper is accompanied by the
	results of numerical experiments.},
}