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《Journal of Data Acquisition and Processing》 2015-01
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NO_X Emission Concentration of Coal-Fired Boiler Prediction Based on Improved PSO Parameter Optimized LSSVM

Sun Weihong;Tong Xiao;Li Qiang;College of Mechanical and Electrical Engineering,China Jiliang University;The Special Equipment Inspection Institute in Xinjiang Uygur Autonomous Region;  
In order to improve the accuracy of NOXemission concentration prediction of the coal-fired boiler and more accurately monitor the NOX pollution,this paper proposes a prediction method based on the least squares support vector machines(LSSVM)and the improved particle swarm optimization(PSO).According to LSSVM forecasting theory as well as the uncertainty of LSSVM parameter selection,an improved PSO algorithm to optimize the parameters of the model is used,a model of NOXemission characteristics is established,and the prediction results are compared with the results of other two methods simultaneously.Results indicate that LSSVM is an effective modeling method which has higher fitting degree;the combination of improved PSO and LSSVM can improve the prediction accuracy and the generalization ability,and LSSVM is superior to the other two parameter optimization algorithms in the NOXemissions concentration forecast.
【CateGory Index】: TP18;TK229.6
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