Attribute reduction algorithm based on cognitive model of granular computing in inconsistent decision information systems

Cilj je ovoga rada istražiti novu metodu redukcije atributa u informacijskim sustavima nekonzistentne odluke. Analizirajući povezanost teorije redukcije atributa i kognitivne znanosti, u radu se predlaže algoritam redukcije atributa zasnovan na kognitivnom modelu granularnog računanja. Analiza algoritma i numerički eksperiment pokazuju vrijednost predloženog algoritma redukcije atributa. Ta se metoda može primijeniti i na konzistentne i nekonzistentne sustave. Predloženi model također daje i novi model i način razmišljanja za proučavanje povezanosti ljudske spoznaje i poimanja. Koristan je za razvoj kognitivnog modela.This article aims to explore a new method of attribute reduction in inconsistent decision information systems. By analyzing the connection of attribute reduction theory and cognitive science, an attribute reduction algorithm based on cognitive model of granular computing is proposed in this paper. Algorithm analysis and numerical experiment show the validity of the proposed attribute reductions algorithm. The method can be applied to both consistent and inconsistent information systems. The proposed model also provides a new model and thinking to study the connection of human’s cognition and notion. It is useful to the development of cognitive model