%0 Journal Article
%T The Unsupervised Classification Using Evolutionary Strategies and Neural Networks<br>基于遗传策略和神经网络的非监督分类方法
%A LI Ming
%A YAN Chao-hua
%A LIU Gao-hang
%A <br>黎明
%A 严超华
%A 刘高航
%J 软件学报
%D 1999
%I 
%X A new unsupervised classification method using evolutionary strategies and fuzzy ART (adaptive resonance theory) neural networks is proposed in this paper. First, fuzzy ART neural networks is trained by original input samples under unsupervised way. Then evolutionary strategies is used to generate new training samples near the clusters boundaries of neural networks. Therefore the weights of fuzzy ART neural networks can be revised and refined by training those new generated samples under supervised way. The proposed method resolves the training problem for ART serial neural networks when there are only less training samples available. Consequently, it enhances the performance of ART serial neural networks and extends their application.
%K ART(adaptive resonance theory)<br>神经网络
%K 遗传算法
%K 非监督分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=7735F413D429542E610B3D6AC0D5EC59&aid=BA8EF821908AEF4C9979539F38C36237&yid=B914830F5B1D1078&vid=F3090AE9B60B7ED1&iid=59906B3B2830C2C5&sid=BBED0819A2A875B8&eid=29AE54A8F28A81D3&journal_id=1000-9825&journal_name=软件学报&referenced_num=2&reference_num=10