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In this paper, we present a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems. For multiple antecedent variables interpolation, the proposed method allows each condition appearing in the antecedent parts of fuzzy rules associated with a weighting factor. The alpha-cuts and transformation techniques are extended to handle the weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems. The proposed method provides us a useful way to deal with weighted fuzzy interpolative reasoning in sparse fuzzy rule-based systems.
Machine Learning and Cybernetics, 2008 International Conference on (Volume:6 )
Date of Conference: 12-15 July 2008