[1]沈葛亮,顾斌杰,潘 丰.基于灰狼优化算法的孪生支持向量回归机[J].南京理工大学学报(自然科学版),2020,44(02):202-208.[doi:10.14177/j.cnki.32-1397n.2020.44.02.011]
 Shen Geliang,Gu Binjie,Pan Feng.Twin support vector regression based on grey wolf optimization algorithm[J].Journal of Nanjing University of Science and Technology,2020,44(02):202-208.[doi:10.14177/j.cnki.32-1397n.2020.44.02.011]
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基于灰狼优化算法的孪生支持向量回归机
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年02期
页码:
202-208
栏目:
出版日期:
2020-04-30

文章信息/Info

Title:
Twin support vector regression based on grey wolf optimization algorithm
文章编号:
1005-9830(2020)02-0202-07
作者:
沈葛亮顾斌杰潘 丰
江南大学 轻工过程先进控制教育部重点实验室,江苏 无锡 214122
Author(s):
Shen GeliangGu BinjiePan Feng
Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education, Jiangnan University,Wuxi 214122,China
关键词:
孪生支持向量回归机 灰狼优化算法 参数优化 模型选择
Keywords:
twin support vector regression grey wolf optimization algorithm parameter optimization model selection
分类号:
TP181
DOI:
10.14177/j.cnki.32-1397n.2020.44.02.011
摘要:
为了解决孪生支持向量回归机的参数寻优问题,提出了一种基于灰狼优化算法的孪生支持向量回归机。该算法将均方根误差和平均绝对误差作为灰狼优化算法的适应度函数,借助灰狼优化算法的全局寻优能力,以目标范围内生成狼群的位置代表不同的孪生支持向量回归机参数取值,通过有限次数迭代和灰狼优化算法的位置更新机制得到孪生支持向量回归机的最优参数。实验结果表明,该算法能够找到合适的参数; 与现有算法相比,该算法的预测性能更佳,寻优时间显著缩短。
Abstract:
In order to solve the problem of parameter optimization for the twin support vector regression(TSVR),a twin support vector regression based on the grey wolf optimization(GWOTSVR)algorithm is proposed here. First,the root mean square error(RMSE)and the mean absolute error(MAE)are selected as the fitness function of the GWO. By resorting to the global optimization capability of the GWO algorithm,the positions of the wolves generated in the target range are used to represent different parameter values of TSVR. Finally,the optimal parameters are obtained by utilizing the position upgrade mechanism of the GWO within finite iterations. The experimental results show that the algorithm in this paper can find suitable parameters,has better prediction performance and costs much less optimization time than the state-of-the-art algorithms.

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备注/Memo

备注/Memo:
收稿日期:2018-12-17 修回日期:2019-04-30
基金项目:国家自然科学基金(31771680)
作者简介:沈葛亮(1993-),男,硕士生,主要研究方向:机器学习,E-mail:17851315189@163.com; 通讯作者:顾斌杰(1980-),男,博士,讲师,主要研究方向:机器学习、发酵过程建模,E-mail:gubinjie1980@jiangnan.edu.cn。
引文格式:沈葛亮,顾斌杰,潘丰. 基于灰狼优化算法的孪生支持向量回归机[J]. 南京理工大学学报,2020,44(2):202-208.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-04-20