Fuzzy model identification based on fuzzy-rule clustering and its application for airfoil noise prediction

摘要

The airfoil noise problem is highly nonlinear, but its prediction is very important to broadband helicopter rotors, wind turbines, and airframe noise. Thus, this paper presents a novel strategy whereby the minimum-of-maximum relative error support vector machine (RE-SVM) is used to improve the approximation ability of a fuzzy airfoil noise prediction system. In the preliminary design stage, the antecedents of the fuzzy rule base are used to cluster the fuzzy rules. Then, those fuzzy rules with the same antecedent are clustered. Next, in each cluster, the fuzzy rule that has the highest degree of confidence is regarded as the cluster center, which becomes the final fuzzy rule. Finally, the consequents of the fuzzy rules are obtained using RE-SVM models. The prediction of airfoil noise demonstrates that the proposed method has high prediction accuracy.