Soft Instrument for the Flue Gas Oxygen of Power Plant Based On Improved SMO Algorithm
Yongjie Zhai1, Hong Qiao, Haili Li, Guorui Ji, Pu Han
1North China Electric Power University
Available Online December 2008.
- 10.2991/jcis.2008.96How to use a DOI?
- Sequential Minimal Optimization (SMO); Support vector regression; Grey; Oxygen; Soft-sensing
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.
- © 2008, 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 - 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 - Proceedings of the 11th Joint Conference on Information Sciences (JCIS 2008) PB - Atlantis Press SP - 571 EP - 577 SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2008.96 DO - 10.2991/jcis.2008.96 ID - Zhai2008/12 ER -