Soft Instrument for the Flue Gas Oxygen of Power Plant Based On Improved SMO Algorithm
Yongjie Zhai 0, Hong Qiao, Haili Li, Guorui Ji, Pu Han
0North China Electric Power University
Available Online December 2008.
- https://doi.org/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.
- 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 -