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• W2012313887 endingPage "1261" @default.
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• W2012313887 abstract "In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems." @default.
• W2012313887 created "2016-06-24" @default.
• W2012313887 creator A5052341134 @default.
• W2012313887 creator A5061831201 @default.
• W2012313887 date "2015-07-01" @default.
• W2012313887 modified "2023-10-18" @default.
• W2012313887 title "Weighted Fuzzy Interpolative Reasoning Based on the Slopes of Fuzzy Sets and Particle Swarm Optimization Techniques" @default.
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• W2012313887 doi "https://doi.org/10.1109/tcyb.2014.2347956" @default.
• W2012313887 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25204003" @default.
• W2012313887 hasPublicationYear "2015" @default.
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