A reduced weighted wang-mendel algorithm using the clustering algorithm to build fuzzy system

The efficiency of the Wang-Mendel (WM) algorithm is severely affected by the number of fuzzy rules and data scale. Thus, this paper proposes a reduced weighted WM algorithm to solve the problem by balancing the completeness and the computation time. The clustering algorithm is first introduced to obtain the cluster centers. Then, only the cluster centers are used to generate fuzzy rules, namely, the most important fuzzy rules are obtained. Finally, the weighted average is used to improve the accuracy of the WM algorithm. The proposed algorithm can save much computation time and storage space. The results of the experiments demonstrate that the proposed algorithm has high efficiency with high precision.