Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)

Research on Modeling Method Based on Least Squares Support Vector Machine

Authors
Jun Xu, Kang Du, Yao hui Zhang, Xiao bing Zhu
Corresponding Author
Jun Xu
Available Online August 2013.
DOI
10.2991/icacsei.2013.159How to use a DOI?
Keywords
Particle swarm optimization algorithm, Support vector machine, Gas sensor, Mathematical modeling
Abstract

A method of support vector machine based on particle swarm optimization was proposed for the question of parameter selecting difficult of least square support vector machine in modeling of gas sensor. Least square support vector machine is used to build the model of gas sensor. Particle swarm optimization arithmetic was introduced to optimize the parameters of support vector machine. The sensor model is tested with the data measured reality. The results prove the accuracy of the model.

Copyright
© 2013, 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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Volume Title
Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
Series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90-78677-74-1
ISSN
1951-6851
DOI
10.2991/icacsei.2013.159How to use a DOI?
Copyright
© 2013, 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/).

Cite this article

TY  - CONF
AU  - Jun Xu
AU  - Kang Du
AU  - Yao hui Zhang
AU  - Xiao bing Zhu
PY  - 2013/08
DA  - 2013/08
TI  - Research on Modeling Method Based on Least Squares Support Vector Machine
BT  - Proceedings of the 2013 International Conference on Advanced Computer Science and Electronics Information (ICACSEI 2013)
PB  - Atlantis Press
SP  - 665
EP  - 667
SN  - 1951-6851
UR  - https://doi.org/10.2991/icacsei.2013.159
DO  - 10.2991/icacsei.2013.159
ID  - Xu2013/08
ER  -