A new support vector machine-based fuzzy system with high comprehensibility

Abstract

This paper proposes a support vector machine (SVM)-based fuzzy system (SVM-FS), which has good comprehensibility as well as satisfactory generalization capability. SVM provides a mechanism to extract support vectors for generating fuzzy IF-THEN rules from training data. In SVM-FS, SVM is used to extract IF-THEN rules; the fuzzy basis function inference system is adopted as the fuzzy inference system. Furthermore, we theoretically analyze the proposed SVM-FS on the rule extraction and the inference method comparing with other fuzzy systems; comparative tests are performed using benchmark data. The analysis and the experimental results show that the new approach possesses high comprehensibility as well as satisfactory generalization capability. © 2007 Springer-Verlag Berlin Heidelberg.