Research on Extreme Learning Machine Algorithm and Its Application to El-Nino/La-Nina Southern Oscillation Model

被引:0
作者
Xing, De [1 ]
Zhang, Weimin [1 ]
Huang, Qunbo [1 ]
Liu, Bainian [1 ]
机构
[1] Natl Univ Def Technol, Acad Ocean Sci & Engn, Changsha, Hunan, Peoples R China
来源
2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 1 | 2016年
关键词
Extreme Learning Machine; ocean-atmospheric oscillator; ENSO;
D O I
10.1109/IHMSC.2016.279
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Since it has the ability to give a faster result than traditional machine learning algorithms, Extreme Learning Machine (ELM) has become increasingly popular in various research fields recently. However, ELM has been worked on the research of computer science and other related areas except for the atmospheric field. This paper uses the ELM algorithm to simulate the forward integrating process of an ocean-atmosphere oscillator model -- El-Nino/La-Nina Southern Oscillation (ENSO). The results show that the ELM algorithm has a good accuracy and efficiency with a quick convergence speed and a strong resistance over observation noises.
引用
收藏
页码:208 / 211
页数:4
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