摘要
模糊神经网络是一种将模糊推理系统嵌入到神经网络推理过程中的神经网络.如何确定模糊神经网络的结构是一个影响神经网络决策性能的关键问题.而模糊集的个数则是确定模糊神经网络结构的关键.许多研究使用模糊聚类来确定模糊集的个数.然而,现有的模糊聚类算法需要预设参数,这就使得整个过程受到了人为干涉.本文提出了一种自适应的模糊聚类方法来确定模糊集的个数.该方法以两个模糊集交界处的样本的模糊程度为标准,利用分裂法获得最终的聚类结果.实验表明,我们的算法在多个经典数据集上的决策结果在精度上超越了以前的算法.
A fuzzy neural network is a learning machine that embeds a fuzzy system (i.e.,fuzzy sets,fuzzy rules) into neural networks.It is crucial to determine the architecture of the networks according to the number of fuzzy sets.In previous works,the number is determined using fuzzy clustering.However,these methods is depended on some parameters which are set artificially.Thus,the methods are somewhat subjective.In this paper,an adaptive clustering method is proposed.It implements splitting technique and limits the number of samples in the intersection of two fuzzy sets to decrease the uncertainty.Finally,the results show that our algorithm brings out significant improvement over existing methods.
作者
王萌
肖创柏
WANG Meng;XIAO Chuang-bai(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《微电子学与计算机》
CSCD
北大核心
2018年第12期8-11,共4页
Microelectronics & Computer
基金
国家自然科学基金(61501008)
北京市自然科学基金(4162007)
北京市科技计划项目(Z171100004717001)
关键词
模糊聚类
模糊神经网络
fuzzy clustering
fuzzy neural networks
