1.Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, Hebei 066004, China
Abstract In order to accurately predict the NOx emissions of circulating fluidized bed boiler,a GSA-SVM model based on support vector machine (SVM) of NOx emission was established on the base of the sample data of circulating fluidized bed boilers in a power plant.The gravitation search algorithm (GSA) was applied to the process of the model parameters optimization because the accuracy and generalization ability of SVM model depended on the parameters.The prediction performance of the model was tested by sample datas under different experimental conditions.The regression model was compared with BP and the SVM model whose parameters were optomizated by particle swarm optimization(PSO) and genetic algorithms(GA). Finally the simulation result shows that the GSA-SVM model has a good recognition ability and generalization ability
NIU Pei-feng,MA Hong-bo,LI Guo-qiang, et al. NOx Emission Characteristic Model for Circulating Fluidized Bed Boilers Based on GSA-SVM[J]. Acta Metrologica Sinica, 2013, 34(6): 602-606.
［1］王春林,周昊,李国能,等.大型电厂锅炉NOx排放特性的支持向量机模型［J］.浙江大学学报, 2006, 40(10): 1788-1791.
［2］刘涵,李琦,刘丁,等.基于最小二乘支持向量机的电站锅炉空预器热点检测系统研究［J］.中国电机工程学报, 2005, 25(3): 147-152.
［3］张龙,熊国良,柳和生,等.基于时变自回归模型与支持向量机的旋转机械故障诊断［J］.中国电机工程学报, 2007, 27(9): 99-103.
［4］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.
［5］王春林,周昊,李国能,等.基于遗传算法和支持向量机的低NOx燃烧优化［J］.中国电机工程学报, 2007, 27(11): 40-44.
［6］Suykens J A K,Vandewalte J. Least squares support vector machine classifiers［J］.Neural Process, 1999, 9（3): 293-300.
［7］Sung-Hwan Min,Jumin Lee,Ingoo Han. Hybrid genetic algorithms and support vector machines for bankruptcy prediction［J］. Expert Systems with Applications, 2006, (31): 652-660.
［8］齐志泉,田英杰,徐志洁.支持向量机中的核参数选择问题［J］.控制工程, 2005, 12(4): 32-37.
［9］Sanchez A D. Advanced support vector machines and kernel methods［J］.Neurocomputing, 2003, 55(1): 5-20.
［10］邓乃杨,田英杰.数据挖掘的新方法-支持向量机［M］. 北京:科学出版社,2004,63-77.
［11］张学工.关于统计学习理论与支持向量机［J］.自动化学报, 2000, 26(1): 32-43.
［12］Rashedi E,Nezamabadi-pour H,Saryazdi S. A Gravitational Search Algorithm［J］. Information Sciences, 2009, 179(13): 2232-2248.
［13］Ruijin Liao, Hanbo Zheng, Grzybowski S, et al. Particle swarm optimization-least squares support vector regression based forecasting model on dissolved gases in oil-filled power transformers［J］. Electric Power Systems Research, 2011, 81(12): 2074-2080.
2013, Vol. 34 
