%0 Journal Article
%T Classification algorithm based on neural network and rough set<br>一种基于粗糙集神经网络的分类算法*
%A GUO Zhi-jun
%A HE Xin
%A WEI Zhong-hui
%A ZHANG Wei-hu
%A LIANG Guo-long
%A <br>郭志军
%A 何昕
%A 魏仲慧
%A 张伟华
%A 梁国龙
%J 计算机应用研究
%D 2011
%I 
%X When neural network has high dimensional inputs, it may have a complex structure and too large systems, and also it may lead to slow convergence. In order to overcame this shortcoming it proposed a neural network based on decision rules (RDRN). It used rough set theory to get the most simple decision rules from the data samples, and then constructed a not fully connected neural network by the semantics of the decision rules. According to the semantics of decision-making rules, it calculated and initialized the network parameters to reduce the number of iterations of network training and to improve the convergence speed. At the same time, the ant colony optimization algorithm was used to find the optimal discrete value of continuous attributes of the net inputs in order to obtain an optimal network structure. Finally, experimental results of the proposed method, the traditional neural network methods and support vector classification methods were compared. Comparison showed that the neural network converges faster and had advantages of more efficient classification.
%K rough set
%K decision rules
%K membership
%K neural network
%K convergence network
%K ant colony optimization algorithm<br>粗糙集
%K 决策规则
%K 隶属度
%K 神经网络
%K 网络收敛
%K 蚁群算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=64BCC9027547701697CE8D5E5E6C1504&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=D40528F59753C0F7&eid=F7B726EE3ACCF328&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12