11th Joint International Conference on Information Sciences

Soft Instrument for the Flue Gas Oxygen of Power Plant Based On Improved SMO Algorithm

Authors
Yongjie Zhai 0, Hong Qiao, Haili Li, Guorui Ji, Pu Han
Corresponding Author
Yongjie Zhai
0North China Electric Power University
Available Online December 2008.
DOI
https://doi.org/10.2991/jcis.2008.96How to use a DOI?
Keywords
Sequential Minimal Optimization (SMO); Support vector regression; Grey; Oxygen; Soft-sensing
Abstract
As to the problem that normal SVM algorithm has a high computational complexity with large scale data and the method of selecting parameters of the study machine is complexity,we improved the SMO algorithm in two aspects of structure and parametric selection to increase operational speed and efficiency of modeling. It used grey theory to select the auxiliary variables and build a model of soft instrument for the flue gas oxygen content in power plant. The simulation with historical data measured by plant show that compared with the normal SMO algorithm the improved algorithm is better in performance.
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Proceedings
11th Joint International Conference on Information Sciences
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2008
ISBN
978-90-78677-18-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/jcis.2008.96How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yongjie Zhai
AU  - Hong Qiao
AU  - Haili Li
AU  - Guorui Ji
AU  - Pu Han
PY  - 2008/12
DA  - 2008/12
TI  - Soft Instrument for the Flue Gas Oxygen of Power Plant Based On Improved SMO Algorithm
BT  - 11th Joint International Conference on Information Sciences
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2008.96
DO  - https://doi.org/10.2991/jcis.2008.96
ID  - Zhai2008/12
ER  -