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.
ZHANG Wei-ping,ZHAO Wen-lei,LI Guo-qiang, et al. Forecasting of Turbine Main Steam Flow Based on Rough Sets and Least Squars Support Vector Machine Regression[J]. Acta Metrologica Sinica, 2015, 36(1): 43-47.
［1］吴海姬,王雷,司风琪,等.基于BP神经网络的主蒸汽流量计算模型［J］.汽轮机技术,2007,49(4): 269-273.
［2］王雷,张瑞青,肖增弘,等.基于SVM的主蒸汽流量回归估计［J］.华东电力,2008,36(12): 89-92.
［3］周建新,王雷,吴海姬,等.基于支持向量回归的大容量机组主蒸汽流量建模［J］.热能动力工程,2008,23(2): 122-126.
［4］王伟,陈殿生,魏洪兴,等.装载机载重测量的支持向量机软测量建模方法［J］.计量学报,2008,29(4): 329-333.
［5］陈果,周伽.小样本数据的支持向量机回归模型参数及预测区间研究［J］.计量学报,2008,29(1): 92-96.
［6］孙永平,朱芳梅,王敏.北仑电厂2号机组DAS系统主蒸汽流量计算模型的修改［J］.浙江电力,2000,9(3): 14-16.
［7］何军民,李明.湘潭电厂300MW机组主蒸汽流量计算模型的修改［J］.湖南电力,2005,25(5): 17-19.
［8］Zou Z H, Tseng T L, Sohn H, et al. A rough set based approach to distributor selection in supply chain management［J］.Expert systems with applications,2011,38(1): 106-115.
［9］雷绍兰,孙才新,周兰,等.属性约简在空间负荷预测中的应用［J］.重庆大学学报,2004,27(3): 85-88.
［10］Sunken J A K,Vandewalle J.Least squares support vector machine classifiers［J］.Neural Processing Letters,1999,9(3): 293-300.
［11］Keerthi S S,Lin C J.Asymptotic behaviors of support vector machines with Gaussian kernel［J］.Neural Computation,2003,15(7):1667-1689.
2015, Vol. 36 
