[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(3):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(3):225-229.
点击复制

基于GEP的最小二乘支持向量机模型参数选择

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 7 期数: 2012年第3期 页码: 225-229 栏目: 学术论文—人工智能基础 出版日期: 2012-06-25
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1,2，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1,2, 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.
参考文献/References:
［1］VAPNIK V N. The nature of statistical learning theory［M］. New York, USA: SpringerVerlag, 1995.
［2］VAPNIK V, LEVIN E, CUN Y L. Measuring the VCdimension of a learning machine［J］. Neural Computation, 1994, 6(5): 851876.
［3］SMOLA A J, SCHOLKOPF B. A tutorial on support vector regression［J］. Statistic and Computing, 2004, 14(3): 199222.
［4］SANCHEZ A D. Advanced support vector machines and kernel methods［J］. Neurocomputing, 2003, 55(1): 520.
［5］SUYKENS J A K, VANDEWALL J. Least squares support vector machine classifiers［J］. Neural Processing Letters, 1999, 9(3): 293300.
［6］PELCKMANS K, SUYKENS J A K, DE MOOR B. Building sparse representations and structure determination on LSSVM substrates［J］. Neurocomputing, 2005, 64: 137159.
［7］郑小霞，钱锋．基于PCA和最小二乘支持向量机的软测量建模［J］．系统仿真学报, 2006, 18(3): 739741.
ZHENG Xiaoxia, QIAN Feng. Soft sensor modeling based on PCA and support vector machines［J］. Journal of System Simulation, 2006, 18(3): 739741.
［8］赵吉文，刘永斌，孔凡让，等．核参数遗传选优的SVM在直线电机建模中的应用［J］．系统仿真学报, 2006, 18(12): 35473549.
?ZHAO Jiwen, LIU Yongbin, KONG Fanrang, et al. Application of SVM with genetic algorithms optimizing kernel parameters in linear motor model［J］. Journal of System Simulation, 2006, 18(12): 35473549.
［9］刘瑞兰，牟盛静，苏宏业，等．基于支持向量机和粒子群算法的软测量建模［J］．控制理论与应用, 2006, 23(6): 895899, 906.
LIU Ruilan, MOU Shengjing, SU Hongye, et al. Modeling soft sensor based on support vector machine and particle swarm optimization algorithms［J］. Control Theory and Applications, 2006, 23(6): 895899, 906.
［10］THADANI K, JAYARAMAN V K, SUNDARARAJAN V. Evolutionary selection of kernels in support vector machines［C］//International Conference on Advanced Computing and Communications. Mangalore, India, 2006： 1924.
［11］FERREIRA C. Gene expression programming in problem solving［C/OL］. ［20101210］. http://www.geneexpressionprogramming.com/webpapers/GEPtutorial.pdf.
［12］FERREIRA C. Gene expression programming: a new adaptive algorithm for solving problems［J］. Complex Systems, 2001, 13(2): 87129.
［13］MITCHELL M. An introduction to genetic algorithms［M］. Cambridge, UK: The MIT Press, 1996: 143164.
［14］钱晓山，阳春华．改进基因表达式编程在股票中的研究与应用［J］．智能系统学报, 2010, 5(4): 303307.
QIAN Xiaoshan, YANG Chunhua. Improved gene expression programming algorithmtested by predicting stock indexes［J］. CAAI Transactions on Intelligent Systems, 2010, 5(4): 303307.
［15］张春晓，张涛．基于最小二乘支持向量机和粒子群算法的两相流含油率软测量方法［J］．中国电机工程学报, 2010, 30(2): 8691.
ZHANG Chunxiao, ZHANG Tao. Soft measurement method for oil holdup of two phase flow based on least squares support vector machine and particle swarm optimization［J］. Proceedings of the CSEE, 2010, 30(2): 8691.
［16］RIVERO D, DORADO J, RABUNAL J, et al. Using genetic programming for artificial neural network development and simplification［C］//Proceedings of the 5th WSEAS International Conference on Computational Intelligence, ManMachine Systems and Cybernetics. Venice, Italy, 2006: 6571.
［17］XU Kaikuo, LIU Yintian, RONG Tang, et al. A novel method for real parameter optimization based on gene expression programming［J］. Applied Soft Computing, 2009, 9(2): 725737.
［18］徐文熙，穆文俊．化工原理(上)［M］．北京：中国石化出版社, 1992.
相似文献/References:
[1]向峥嵘,陈庆伟.基于小波和LSSVM的软测量建模方法[J].智能系统学报,2010,5(1):63.
　XIANG Zheng-rong,CHEN Qing-wei.An approach to soft sensor modeling based onwavelets and a least square support vector machine[J].CAAI Transactions on Intelligent Systems,2010,5():63.
[2]钱晓山,阳春华.改进基因表达式编程在股票中的研究与应用[J].智能系统学报,2010,5(4):303.
　QIAN Xiao-shan,YANG Chun-hua.Improved gene expression programming algorithm tested by predicting stock indexes[J].CAAI Transactions on Intelligent Systems,2010,5():303.
[3]彭昱忠,元昌安,李洁,等.个体最优共享GEP算法及其气象降水数据预测建模[J].智能系统学报,2016,11(3):401.[doi:10.11992/tis.201603035]
　PENG Yuzhong,YUAN Changan,LI Jie,et al.Individual optimal sharing GEP algorithm and its application in forecast modeling of meteorological precipitation[J].CAAI Transactions on Intelligent Systems,2016,11():401.[doi:10.11992/tis.201603035]
[4]吴晗,王士同.不完整数据分类与缺失信息重要性识别特权LSSVM[J].智能系统学报,2023,18(4):743.[doi:10.11992/tis.202202026]
　WU Han,WANG Shitong.Privileged LSSVM for classification and simultaneous importance identification of missing information on incomplete data[J].CAAI Transactions on Intelligent Systems,2023,18():743.[doi:10.11992/tis.202202026]

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，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，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
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com