基于论域空间模糊划分的粗集神经网络  

Rough Set Neural Network Based on Fuzzy Partition in Universal Space

作　　者：许翔[1] 张东波[1] 王耀南[2] 刘子文[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105 [2]湖南大学电气与信息工程学院,长沙410082

出　　处：《计算机工程》2010年第21期199-201,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60835004)

摘　　要：针对粗集神经网络构建过程中的论域空间划分问题,提出一种基于模糊聚类的论域划分方法。将带交叉变异算子的粒子群优化算法(PSO)与模糊C-均值聚类算法(FCM)相结合,给出一种新的模糊聚类算法CMPSO-FCM,该算法具有良好的搜索能力和聚类效果。提出一种基于信息熵的模糊粗糙集决策规则获取方法,并用获取的规则指导粗集神经网络的构建。实验结果表明,该方法构造的神经网络具有更精简的结构、较好的分类精度和泛化能力。Aiming at the problem of the universal space partition in the process of constructing rough set neural network, this paper proposes an universe of discourse method based on fuzzy clustering. A modified PSO algorithm with crossover and mutation operators is combined with FCM algorithm. And a new fuzzy clustering algorithm（CMPSO-FCM） is proposed. The searching capability and clustering effectiveness are improved by the new algorithm. A set of fuzzy rough decision rules are acquired by entropy method, and a rough set neural network is designed under these decision rules. Experimental results show that this method has superiorities at the aspect of structure, classification precision and generalization.

关 键 词：粗集神经网络 模糊聚类 PSO算法 FCM算法 信息熵 属性约简 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]