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• W4320481392 abstract "In this paper, a new two-dimensional gaussian hierarchical priority fuzzy system (TGHPFS) is proposed to handle interval-valued data. TGHPFS first performs hierarchical clustering of the average value of interval-valued data in each dimension to generate two-dimensional gaussian membership functions of two-level rules. The two levels of rules are associated by calculating the activation strength of the second-level rules to the first-level rules and setting the connection threshold. The regularized least squares method is used to optimize the consequents of the second-level rules. The two-dimensional gaussian membership function designed in this paper is used to model the antecedents of interval-valued data, solving the correlation problem between the left and right values of interval-valued data. The effectiveness of TGHPFS is validated using real-world datasets, and the proposed method is compared with other latest methods to show the superiority of TGHPFS." @default.
• W4320481392 created "2023-02-14" @default.
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• W4320481392 date "2023-06-01" @default.
• W4320481392 modified "2023-10-17" @default.
• W4320481392 title "Two-dimensional Gaussian hierarchical priority fuzzy modeling for interval-valued data" @default.
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• W4320481392 doi "https://doi.org/10.1016/j.ins.2023.02.026" @default.
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