%0 Journal Article
%T Intelligent fault diagnosis for seeker based on least squares support vector machine with genetic algorithm<br>基于遗传优化最小二乘支持向量机的导引头故障诊断
%A LI Xuan
%A LI Jian-hong
%A HE Yu-zhu
%A XU Jin-jiu
%A <br>李轩
%A 李建宏
%A 何玉珠
%A 许金玖
%J 计算机应用研究
%D 2011
%I 
%X In order to improve the correct rate of seeker fault diagnosis, this paper proposed a multi-class classification model for seeker based on LSSVM (least squares support vector machine) optimized by the improved genetic algorithm. The model constructed the multi-class LSSVM classifiers of two layers according to the one-against-one strategy and the improved vote method. And the genetic algorithm which was improved by an adaptive search strategy with a variable step length was employed to select the kernel function parameter and the regularization parameter of LSSVM. Then the data of diagnosing missile seeker were brought into the model to test its functions. Result is shown that the method has higher diagnosis accuracy and computational efficiency by comparing with the standard SVM and BP neural network diagnosing method.
%K fault diagnosis
%K Least Squares Support Vector Machine
%K Genetic Algorithm<br>故障诊断
%K 最小二乘支持向量机
%K 遗传算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=F8999BE824DE92746D686B36D51A4754&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=38B194292C032A66&sid=7E7F5B01D43BD73F&eid=E23857687CD2EE51&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=12