直觉模糊感知器及其收敛性  

An Intuitionistic Fuzzy Perceptron and Its Finite Convergence

作　　者：李龙[1] 赵蓉[1] 

机构地区：[1]衡阳师范学院数学与计算科学系,湖南衡阳421002

出　　处：《衡阳师范学院学报》2014年第6期5-10,共6页Journal of Hengyang Normal University

基　　金：湖南省自然科学基金项目:模糊神经网络学习算法及其收敛性分析(14JJ6039);湖南省教育厅青年项目:模糊神经网络光滑化算法和网络结构优化的研究(13B004);湖南省大学生研究性学习和创新性实验计划项目(CX1407)

摘　　要：将直觉模糊集与感知器网络相结合,提出了一种用来分类的直觉模糊感知器模型,同时构造了一种新的学习算法,并证明了在训练样本直觉模糊可分和相应的条件下,该学习算法是有限收敛。An intuitionistic fuzzy perceptron is proposed by combining intuitionistic fuzzy sets with peceptron network for classification. Furthermore, a new learning algorithm is constructed to train this network. The learning algorithm has an advantage, as proved in this paper, and that it converges in a finite number of steps if the training patterns are intuitionistic fuzzily separable and some other conditions hold.

关 键 词：直觉模糊感知器 有限收敛 学习算法 

分 类 号：O24[理学—计算数学]