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- W2978365979 abstract "Fuzzy Rough Cognitive Network, which is constructed on the theory of fuzzy rough set and fuzzy cognitive maps, have shown good performance in the decision-making. As we know, traditional fuzzy rough set theory concerns the satisfaction degree between objects in universe. Nevertheless, in reality, decision making problems involving the relation which describe the dissatisfaction degree between objects to counter-property are quite common. This means that there are some hidden information may not be fully utilized. For this reason, we employ a novel mixture decision model, named Bipolar Fuzzy Rough Cognitive Networks(BiFRCN) in this paper. BiFRCN is based on bipolar fuzzy rough set theory and fuzzy cognitive maps by making full use of bipolar fuzzy rough granular information according to three-way decision model. Due to BiFRCN not only focus on satisfaction degree, but also dissatisfaction degree, simulation experiments show that BiFRCN is good and robust in functionality and performance compare with traditional methods." @default.
- W2978365979 created "2019-10-10" @default.
- W2978365979 creator A5028749549 @default.
- W2978365979 date "2019-07-01" @default.
- W2978365979 modified "2023-09-24" @default.
- W2978365979 title "Biploar Fuzzy Rough Cognitive Network" @default.
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- W2978365979 doi "https://doi.org/10.1109/ijcnn.2019.8852232" @default.
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