改进LS-SVM的直吹式制粉出力软测量建模  

Soft sensor modeling for mill output of direct fired system based on improved least squares support vector machines

作　　者：冯磊华[1,2] 桂卫华[1] 杨锋 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410083 [2]长沙理工大学"可再生能源电力技术"湖南省重点实验室,湖南长沙410114 [3]湖南江河机电自动化工程有限公司,湖南长沙410013

出　　处：《电机与控制学报》2011年第11期79-82,共4页Electric Machines and Control

基　　金：国家973计划资助项目(2009CB219803-03);长沙理工大学"动力机械与工程"学科基金项目(201006)

摘　　要：针对火电厂双进双出钢球磨煤机直吹式制粉系统这一大滞后、强非线性系统,其制粉出力较难直接测量的问题,在Suykens的最小二乘支持向量机稀疏化算法的基础上,提出一种更好的改进方式,即在删除一些过大或过小的训练样本的同时,也将变化率过大的数据删除,避免了坏样本对模型的影响,简化了LS-SVM模型。改进后的最小二乘支持向量机算法被用于建立双进双出钢球磨煤机直吹式制粉出力的软测量模型。分别对改进前后的模型进行仿真验证,改进后的均方误差为0.022 7,比改进前减小了0.011 9,其收敛速度也更快,更加适用于在线学习。According to the problem that the direct-fired system with duplex inlet and outlet ball pulverizer is a large time delay and strongly nonlinear system,its mill output is difficult to be directly measured.In this paper,a better improvement algorithm was proposed based on the Suykens＇ sparseness method of least squares support vector machines（LS-SVM）.This new method is that when deleting some too large or too small training samples,the large variety-rate data were also deleted.This has simplified the LS-SVM model and avoided the bad sample＇s effect.The improved LS-SVM was used to establish the soft sensor model of mill output in direct-fired system with duplex inlet and outlet ball pulverizer.The before and after improvement LS-SVM algorithm was respectively simulated to verify it＇s precision.The mean square error after improvement is 0.0227,reduced 0.0119 than before,and it＇s learning speed is faster.So it is more suitable to study on-line.

关 键 词：改进最小二乘支持向量机 双进双出钢球磨煤机 直吹式制粉系统 制粉出力 软测量建模 

分 类 号：TK323[动力工程及工程热物理—热能工程]