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• W3181128888 abstract "Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method." @default.
• W3181128888 created "2021-07-19" @default.
• W3181128888 creator A5026019509 @default.
• W3181128888 creator A5051857096 @default.
• W3181128888 creator A5055267165 @default.
• W3181128888 date "2021-07-09" @default.
• W3181128888 modified "2023-09-27" @default.
• W3181128888 title "The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map" @default.
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• W3181128888 doi "https://doi.org/10.3233/ida-205271" @default.
• W3181128888 hasPublicationYear "2021" @default.
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