Abstract: Relative to the defect of fuzzy membership as a function of distance between the point and its class center in feature space for some current fuzzy support vector machines，a new method to calculate the fuzzy membership is proposed.It first utilizes normal distribution based on the probability of membership function π to calculate the ambiguity，due to the normal distribution feature，takes the distribution of the data into consideration to calculate the membership.The outputs can reflect the characteristic of the membership more accurately.Experiments show that compared with SVM and FSVM，this method has a better performance on reducing the effect of outliers and significantly improves the classification accuracy.

Key words: Support Vector Machine（SVM）, Fuzzy Support Vector Machine（FSVM）, normal distribution, probability, membership, function π

摘要： 针对当前模糊支持向量机（FSVM）一般使用特征空间样本与类中心之间的距离构建隶属度函数的不足，提出了一种计算FSVM的隶属度的新方法。首次使用基于正态分布概率的π型隶属度函数来计算隶属度，根据正态分布的特性，在考虑数据分布规律的同时求得数据点的隶属值，使得求得的数据能够更加准确地反应数据的特点，进而获得更好的分类函数。实验表明，这种方法较SVM和FSVM相比，降低了噪声数据的影响，并且有效地提高了分类的准确率。

关键词: 支持向量机, 模糊支持向量机, 正态分布, 概率, 隶属度, π型函数