[1]钱晓山,阳春华.基于GEP的最小二乘支持向量机模型参数选择[J].智能系统学报,2012,7(03):225-229.
 QIAN Xiaoshan,YANG Chunhua.A parameter selection method of a least squares support vector machine based on gene expression programming[J].CAAI Transactions on Intelligent Systems,2012,7(03):225-229.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第7卷
期数:
2012年03期
页码:
225-229
栏目:
出版日期:
2012-06-25

文章信息/Info

Title:
A parameter selection method of a least squares support vector machine based on gene expression programming
文章编号:
1673-4785(2012)03-0225-05
作者:
钱晓山12阳春华1
1.中南大学 信息科学与工程学院,湖南 长沙 410083;
2.宜春学院 物理科学与工程技术学院,江西 宜春 336000
Author(s):
QIAN Xiaoshan12 YANG Chunhua1
1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Physical Science and Technology College, Yichun University, Yichun 336000, China
关键词:
基因表达式编程最小二乘支持向量机参数选择粒子群算法遗传算法
Keywords:
gene expression programming (GEP) least squares support vector machine (LSSVM) parameter selection particle swarm optimization (PSO) genetic algorithm (GA)
分类号:
TP181
文献标志码:
A
摘要:
针对最小二乘支持向量机的多参数寻优问题,提出了一种基于基因表达式编程的最小二乘支持向量机参数优选方法.该算法将最小二乘支持向量机参数(C,σ)样本作为GEP的基因,按其变异算子随着进化代数和染色体所含基因数目动态变化的机制执行,其收敛速度和精确度大大提高.并与基于粒子群算法和遗传算法参数优选方法比较,通过标准测试函数验证了该算法的拟合误差最低.最后用其建立氧化铝生产蒸发过程参数预测模型,应用工业生产数据进行验证,实验结果表明该方法有效且获得了满意的效果.
Abstract:
To solve the multiparameter optimization problem of least squares support vector machines (LSSVM), a parameter optimization method based on gene expression programming (GEP) was proposed. The parameter (C,σ) samples of LSSVM were selected to be genes for GEP according to the mechanism of the dynamic change of the mutation operator with the gene number of the genome and the number of evolutionary generations. As a result, the convergence rate and accuracy were greatly increased. The new method was compared with other parameter optimization methods based on particle swarm optimization (PSO) and a genetic algorithm (GA) by several standard test functions, and the results show that the proposed method obtains the minimum fitting error. Finally, a parameter prediction model of the evaporation process of alumina production was established; the verification results using the industrial production data show that the method is effective and the result is satisfactory.

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备注/Memo

备注/Memo:
收稿日期: 2010-12-13.网络出版日期:2012-04-25.
基金项目:国家自然科学基金资助项目(60874069);国家“863”计划资助项目(2009AA04Z124, 2009AA04Z137).
通信作者:钱晓山.E-mail: qianxiaoshan@126.com.
作者简介:
钱晓山,男,1980年生,讲师,博士研究生,主要研究方向为复杂工业过程建模、优化控制.
 阳春华,女,1965年生,教授,博士生导师,博士,享受国家政府特殊津贴专家,中国有色金属学会计算机学术委员会委员兼秘书长,中国自动化学会理事、应用专业委员会委员、技术过程故障诊断与安全性专业委员会委员,中国人工智能学会智能控制与智能管理专业委员会委员,湖南省自动化学会常务理事.主要研究方向为复杂工业过程建模、优化控制、智能信息处理.完成或在研国家自然科学基金、国家“863”与“973”计划、国家高技术产业化等科研项目36项.曾获国家科技进步二等奖2项,省部级科技进步奖16项,教育部首届“新世纪优秀人才”,第5届湖南省青年科技奖,湖南省“十大杰出女性”.申请国家发明专利19项、授权6项,申请软件著作权8项,发表学术论文300余篇,其中被SCI、EI检索110余篇.
更新日期/Last Update: 2012-09-05