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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.

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