A new weighted fuzzy rule interpolation method based on GA-based weights-learning techniques
Shyi‐Ming Chen, Yu‐Chuan Chang · 2010
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 the fuzzy rules for the proposed weighted fuzzy interpolative reasoning method. The proposed weighted fuzzy interpolative reasoning method using the optimally learned weights by the proposed GA-based weights-learning method gets smaller error rates than the existing methods for dealing with the computer activity prediction problem. The proposed method provides us with a useful way for fuzzy rule interpolation in sparse fuzzy rule-based systems.