The fuzzy rule base is essential for the performance of fuzzy systems. However, because of many uncertain effects and a great deal of noise in practical industrial applications, the Wang-Mendel (WM) algorithm may extract bad fuzzy rules or fuzzy rules with low confidence that decrease model performance. Moreover, the efficiency of the WM algorithm is affected by scale of the dataset. To address these issues, this paper proposes an improved WM algorithm that optimizes samples before training the fuzzy system using the clustering algorithm. Furthermore, the proposed method enhances its accuracy by using the weighted distance among samples to extract the complete fuzzy rule base. Moreover, the proposed method can adaptively calculate the number of fuzzy partitions and standard deviation of the Gaussian membership function of each variable. Experiments demonstrate that the proposed method performs well for the datasets.
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