%0 Journal Article
%T Quantum-inspired evolutionary tuning of SVM parameters<br>受限空间细水雾作用下烟气温度变化规律研究
%A Zhiyong Luo
%A Ping Wang
%A Yinguo Li
%A Wenfeng Zhang
%A Wei Tang
%A Min Xiang
%A <br>Zhiyong Luo
%A Ping Wang
%A Yinguo Li
%A Wenfeng Zhang
%A Wei Tang
%A Min Xiang
%J 自然科学进展
%D 2008
%I 
%X Common used parameters selection method for support vector machines (SVM) is cross-validation, which needs a long-time complicated calculation. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum-inspired evolutionary algorithm (QEA). QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of least squares support vector machines (LS-SVM) can be adjusted using quantum-inspired evolutionary optimization. Classification and function estimation are studied using LS-SVM with wavelet kernel and Gaussian kernel. The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM , and the improved LS-SVM with wavelet kernel can provide better precision.
%K quantum-inspired evolutionary algorithm (QEA)
%K parameters tuning
%K support vector machines (SVM)
%K least squares support vector machines (LS-SVM)<br>细水雾
%K 烟气
%K 温度
%K 受限空间
%K 细水雾
%K 作用
%K 火灾烟气
%K 温度变化规律
%K 设计参数
%K 理论
%K 三维数学模型
%K 空间分布
%K 数学关系
%K 数据推导
%K 利用实验
%K 机理
%K 烟气温度
%K 水雾抑制
%K 影响
%K 降温速率
%K 因素
%K 水平距离
%K 火源
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=504AF8C1E5476CA7C4EC9DF6FEAC14AC&aid=3D558D35B6AD22136B1667398233CD56&yid=67289AFF6305E306&vid=13553B2D12F347E8&iid=E158A972A605785F&sid=D93AD940782892D0&eid=03436AC72A659ACA&journal_id=1002-008X&journal_name=自然科学进展&referenced_num=0&reference_num=10