一种输入数据为模糊数的模糊支持向量机

发布时间：2018-06-05 06:05

本文选题：模糊支持向量机 + 模糊数　；参考：《计算机工程与应用》2017年20期

【摘要】：支持向量机所处理的数据绝大多数是精确值,但当训练样本中含有模糊信息时,支持向量机将无能为力。基于此,针对输入数据是模糊数的分类问题,提出一种带有去模糊函数的模糊支持向量机(FSVM~*)。该算法采用模糊数间的距离作为模糊数去模糊的度量,从而构造去模糊函数将模糊值转化为精确值,同时将去模糊函数与模糊支持向量机相结合完成模糊数据的分类。数值结果表明:相比Forghani提出的FSVDD~*算法,该算法更有效。
[Abstract]:Most of the data processed by SVM are accurate, but when the training sample contains fuzzy information, SVM will be powerless. In view of the problem that the input data is fuzzy number, a fuzzy support vector machine (FSVM) with deblurring function is proposed. In this algorithm, the distance between fuzzy numbers is used as the measure of fuzzy number de-fuzziness, and the fuzzy function is constructed to convert the fuzzy value into the exact value. At the same time, the fuzzy data classification is realized by combining the de-fuzzy function with the fuzzy support vector machine. The numerical results show that this algorithm is more effective than the FSVDDN * algorithm proposed by Forghani.
【作者单位】：北方民族大学数学与信息科学学院;
【基金】：国家自然科学基金(No.61261043) 北方民族大学研究生创新项目(校研究生发[2014]6号)
【分类号】：O159;TP18

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