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。
The data that Support Vector Machine(SVM)deals with are mostly precise values,but the SVM cannot be utilizedwhen training samples involving in fuzzy information.Based on this,in response to the classification problem thatinput data are fuzzy numbers,a novel Fuzzy SVM(FSVM*)with defuzzification function is proposed.This algorithmconstructs defuzzification function by taking the distance between fuzzy numbers as the metric for defuzzification offuzzy numbers to convert fuzzy numbers into precise values,and classifies the fuzzy data by using defuzzification functionand fuzzy SVM in combination at the same time.The experimental results show that the model in this paper is moreeffective compared to the FSVDD*proposed by Forghani.
作者
张新亚
沈菊红
刘楷
ZHANG Xinya;SHEN Juhong;LIU Kai(School of Mathematics and Information Science, Beifang University of Nationalities, Yinchuan 750021, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第20期122-127,共6页
Computer Engineering and Applications
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)
关键词
模糊支持向量机
模糊数
去模糊函数
距离
fuzzy support vector machine
fuzzy number
defuzzification function
distance
