An improved hybrid intelligent extreme learning machine

被引：0
|
作者：
Lin, Mei-Jin [1 ,2 ]
Luo, Fei [2 ]
Su, Cai-Hong [1 ]
Xu, Yu-Ge [2 ]
机构：
[1] Mechanical and Electrical Engineering College, Foshan University, Foshan,528000, China
[2] School of Automation Science and Engineering, South China University of Technology, Guangzhou,510640, China
来源：
Kongzhi yu Juece/Control and Decision | 2015年 / 30卷 / 06期
关键词：
D O I：
10.13195/j.kzyjc.2014.0321
中图分类号：
学科分类号：
摘要：
An improved hybrid intelligent algorithm based on differential evolution(DE) and particle swarm optimization (PSO) is proposed. The performance of DEPSO algorithm is verified by simulations on 10 benchmark functions. Then, an improved learning algorithm named DEPSO extreme learning machine(DEPSO-ELM) algorithm for single hidden layer feedforward networks(SLFNs) is proposed. In DEPSO-ELM, DEPSO is used to optimize the network hidden node parameters, and ELM is used to analytically determine the output weights. Simulation results of 6 real world datasets regression problems show that the DEPSO-ELM algorithm performs better than DE-ELM and SaE-ELM. Finally, the effectiveness of the DEPSO-ELM algorithm is verified in the prediction of NC machine tool thermal errors. ©, 2015, Northeast University. All right reserved.
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页码：1078 / 1084
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