1.Department of Electromechanical Engineering, Qinhuangdao Institute of Technology, Qinhuangdao, Hebei 066100, China;
2.Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract：A new prediction method is put forward in view of the shortages of traditional main steam flow calculation method,which combines the advantages both rough set theory and least squares support vector regression algorithm. Therefore,this new method is called RS-LSSVR. In RS-LSSVR,the attributes reduction of input variable by genetic algorithm is carried out on the ROSETTA V1.4.41 research experimental platform,then the main steam flow prediction model is established by LSSVR algorithm.The simulation results show that the method based on RS-LSSVR has better prediction precision and generalization ability compared with BP algorithm, support vector regression algorithm and LSSVR algorithm without treated by the RS theory.Moreover,the modeling speed increases significantly.
张维平,赵文蕾,李国强,牛培峰. 基于粗糙集与最小二乘支持向量回归的汽轮机主蒸汽流量预测[J]. 计量学报, 2015, 36(1): 43-47.
ZHANG Wei-ping,ZHAO Wen-lei,LI Guo-qiang,NIU Pei-feng. Forecasting of Turbine Main Steam Flow Based on Rough Sets and Least Squars Support Vector Machine Regression. Acta Metrologica Sinica, 2015, 36(1): 43-47.
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2015, Vol. 36 
