基于布谷鸟搜索算法参数优化的组合核极限学习机

吉林大学学报(理学版) ›› 2019, Vol. 57 ›› Issue (5): 1185-1192.

基于布谷鸟搜索算法参数优化的组合核极限学习机

张森悦^1,2, 谭文安¹, 王楠³

1. 南京航空航天大学计算机科学与技术学院, 南京 211106;2. 沈阳航空航天大学经济与管理学院, 沈阳 110136; 3. 吉林财经大学管理科学与信息工程学院, 长春 130117

收稿日期:2018-12-11 出版日期:2019-09-26 发布日期:2019-09-20
通讯作者: 谭文安 E-mail:watan@sspu.edu.cn

Combined Kernel Extreme Learning Machine Based onCuckoo Search Algorithm Parameter Optimization

ZHANG Senyue^1,2, TAN Wen’an¹, WANG Nan³

1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106,China;
2. College of Economics and Management, Shenyang Aerospace University, Shenyang 110136, China;
3. College of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China

Received:2018-12-11 Online:2019-09-26 Published:2019-09-20
Contact: TAN Wen’an E-mail:watan@sspu.edu.cn

摘要/Abstract

摘要： 针对单核极限学习机在泛化性能上存在一定局限性的问题, 提出将再生核函数与多项式核函数相结合, 建立一种新的组合核极限学习机模型, 使其具有全局核与局部核的优点, 并选择布谷鸟搜索算法对其参数进行优化选择. 仿真实验结果表明, 采用基于再生核的组合核函数作为极限学习机的核函数可行, 在实验数据集的多值分类和回归问题上, 与传统支持向量机及单核极限学习机相比, 该模型具有更好的泛化性能.

关键词: 布谷鸟搜索算法, 核极限学习机, 组合核函数

Abstract: Aiming at the problem of the limitations of the generalization performance of the single kernel extreme learning machine, we proposed to combine the reproducing kernel function with the polynomial kernel function to establish a new combined kernel extreme learning machine model, which had the advantages of global and local kernels, and selected cuckoo search algorithm to optimize its parameters. The simulation results show that it is feasible to use the combined kernel function based on the reproducing kernel as the kernel function of extreme learning machine. Compared with traditional support vector machine and single kernel extreme learning machine, the model has better generalization performance in multi\|valued classification and regression of experimental datasets.

Key words: cuckoo search algorithm, kernel extreme learning machine, combined kernel function

中图分类号:

TP391

张森悦, 谭文安, 王楠. 基于布谷鸟搜索算法参数优化的组合核极限学习机[J]. 吉林大学学报(理学版), 2019, 57(5): 1185-1192.

ZHANG Senyue, TAN Wen’an, WANG Nan. Combined Kernel Extreme Learning Machine Based onCuckoo Search Algorithm Parameter Optimization[J]. Journal of Jilin University Science Edition, 2019, 57(5): 1185-1192.