%0 Journal Article
%T Control chart patterns recognition based on support vector machine<br>基于支持向量机的控制图模式识别
%A WU Shao-xiong
%A HUANG En-zhou
%A <br>吴少雄
%A 黄恩洲
%J 计算机应用
%D 2007
%I 
%X To improve patterns recognition performance of control chart,a pattern-recognition method based on hybrid kernel was presented.In the structure modeling,the one-against-one-algorithm multi-class classification Support Vector Machine(SVM) was applied,and genetic algorithm was used to optimize the parameters of SVM.The simulation results and application show that the performance of SVM with hybrid kernel is superior to single common kernel,probabilistic neural network and Wavelet Probabilistic Neural Network(WPNN) in the aggregate classification rate and type I error.What's more,it has simpler structure and quicker convergence that it could be applied in the on-line process quality control.
%K control chart
%K patterns recognition
%K multi-class classification
%K SVM(Support Vector Machine)<br>控制图
%K 模式识别
%K 多类分类
%K 支持向量机
%K 支持向量机
%K 控制图模式识别
%K support
%K vector
%K machine
%K based
%K recognition
%K patterns
%K chart
%K 工序质量控制
%K 实时在线
%K 生产现场
%K 泛化能力
%K 小波概率神经网络
%K 混合核函数
%K 错判
%K 识别率
%K 应用结果
%K 仿真
%K 参数
%K 算法优化
%K 遗传
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=7EB4F89C56892AD62E6277073B2BC7C4&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=CA4FD0336C81A37A&sid=1D0FA33DA02ABACD&eid=0401E2DB1F51F8DE&journal_id=1001-9081&journal_name=计算机应用&referenced_num=3&reference_num=13