基于改进LS-SVM的球磨机制粉出力软测量建模  

Soft sensor modeling for mill output of ball pulverizer based on an improved least squares support vector machines

作　　者：郭云峰[1] 王运民[2] 杨锋 

机构地区：[1]新华水利水电投资公司,北京100053 [2]长沙理工大学,长沙410114 [3]湖南江河机电自动化工程有限公司,长沙410013

出　　处：《微计算机信息》2012年第8期53-55,共3页Control & Automation

基　　金：基金申请人:王运民;项目名称:大型燃煤发电机组变工况特性及能耗控制方法;基金颁发部门:国家973计划委员会(2009CB219803-03)

摘　　要：针对火电厂双进双出钢球磨直吹式制粉系统出力较难直接测量的问题,本文在Suykens的最小二乘支持向量机(least squares support vector machine,LS-SVM)稀疏化算法的基础上,提出了一种更好的改进方式,并将改进后的LS-SVM算法对双进双出钢球磨直吹式制粉系统出力建立软测量模型,通过对算法改进前后模型仿真结果的对比分析可知,改进后的LS-SVM算法学习收敛速度更快,误差更小,更加适用于在线学习,并为制粉系统的在线优化控制打下了良好的基础。According to the problem that mill output is difficult to directly measured, a new improved algorithm was proposed based on the Suykens＇ sparseness method of least squares support vector machines （LS-SVM）. The improved LS-SVM was used to estab- lish the soft sensor model of mill output in direct-fired system with duplex inlet and outlet ball pulverizer. The two models of before and after LS-SVM algorithm improvement was compared with each other. Simulation results show that the learning speed is faster and the error is less of after improvement than before. It is more suitable for study on-line. This will lay a good foundation for the optimal control online of pulverizing systems.

关 键 词：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]