Research on Extreme Learning Algorithm and Its Application to Atmospheric Nonlinear Systems

DOI

10.2991/ameii-16.2016.158How to use a DOI?

Keywords

Extreme learning machine, Nonlinear chaotic system, Lorenz63

Abstract

Extreme learning machine (ELM) algorithm, which becomes more and more popular in the area of artificial intelligence for the past few years, is faster than the traditional machine learning algorithms, especially than the single hidden layer feed-forward neural networks (SLFNs). However, ELM is merely commonly used in the field of computer science or other hot areas. This paper investigates the ability of ELM to emulate the atmospheric nonlinear systems. The performance of ELM on emulating the nonlinear chaotic system - Lorenz63 is analyzed. The results show that ELM can accurately and quickly simulate the Lorenz63 forecast field at different forecast length, and thus providing a new idea for solving kinds of atmospheric nonlinear equations.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qunbo Huang
AU  - Bainian Liu
AU  - Weimin Zhang
AU  - Jingzhe Sun
AU  - Mengbin Zhu
AU  - Shiwei Lin
AU  - Weifeng Wang
PY  - 2016/04
DA  - 2016/04
TI  - Research on Extreme Learning Algorithm and Its Application to Atmospheric Nonlinear Systems
BT  - Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
PB  - Atlantis Press
SP  - 811
EP  - 816
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-16.2016.158
DO  - 10.2991/ameii-16.2016.158
ID  - Huang2016/04
ER  -