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This paper proposes a weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on GA-based weights-learning techniques. It can deal with fuzzy rule interpolation with weighted antecedent variables appearing in the antecedents of fuzzy rules. We also propose a GA-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of...
A boosted genetic fuzzy classifier (BGFC) is presented in this paper for land cover classification from VHR multispectral images. The proposed system is constructed through a two stage process. The first stage incrementally generates a fuzzy rule base, through repeated invocations of an evolutionary rule extracting algorithm. A boosting algorithm is employed, forcing new rules to focus on uncovered...
This paper tries to apply a genetic algorithm-based method to fuzzy rule-base system for fuzzy classification with minimum fuzzy rules, which simultaneously enhances or maintain the performance of the fuzzy classification system with fuzzy rule-base. That is, the optimization is included with the minimization of the number of the extracted fuzzy rules and the maximization of the performance of the...
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