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

DOI

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.

Copyright

© 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/).

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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  -