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对学习矢量量化神经网络中“死”点问题的研究
引用本文:冯乃勤,南书坡,郭战杰. 对学习矢量量化神经网络中“死”点问题的研究[J]. 计算机工程与应用, 2009, 45(4): 64-66. DOI: 10.3778/j.issn.1002-8331.2009.04.018
作者姓名:冯乃勤  南书坡  郭战杰
作者单位:河南师范大学,计算机与信息技术学院,河南,新乡,453007
摘    要:竞争型神经网络已经在模式识别、分类等方面得到了广泛的应用,与传统的聚类方法相比具有巨大优势,但是在许多方面还存在不足,需要进一步完善。在Kohonen提出的学习矢量量化网络(Learning Vector Quantization Network,LVQ)的基础上,引入阈值学习规则,较好地解决了该类网络中遇到"死"点时训练误差偏大的问题,最后通过Matlab编程实现。

关 键 词:学习矢量量化网络  阈值  死点
收稿时间:2008-01-11
修稿时间:2008-5-5 

Research of "blind" spot in LVQ network
FENG Nai-qin,NAN Shu-po,GUO Zhan-jie. Research of "blind" spot in LVQ network[J]. Computer Engineering and Applications, 2009, 45(4): 64-66. DOI: 10.3778/j.issn.1002-8331.2009.04.018
Authors:FENG Nai-qin  NAN Shu-po  GUO Zhan-jie
Affiliation:FENG Nai-qin,NAN Shu-po,GUO Zhan-jie Faculty of Computer & Information Science,Henan Normal University,Xinxiang,Henan 453007,China
Abstract:Competitive neural network has been widely used in the pattern recognition,classification and other aspects,and shows the great advantages compared with the traditional clustering methods.But the competitive neural network is still inadequate in many aspects,and needs to be further improved.Through the introduction of threshold value learning rules,this paper resolves the issue of getting the training error in the face of such network's blind spot,on the basis of Kohonen's Learning Vector Quantization Netwo...
Keywords:Learning Vector Quantization Network(LVQ)  threshold value  blind spot
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