摘要
针对火电厂热力参数失效、优化运行的问题,提出了基于支持向量机的软仪表.仿真表明,支持向量机方法与较为典型的RBF神经网络相比有着明显的优势,这样的热力参数软仪表的建立对于电厂的经济运行有着重大的意义.
A SVM-based soft sensor, which can solve the problem of the invalidation of thermal parameters and optimal running is put forward. Experiments show that the support vector machine (SVM) method has great advantage than radial basis function neural networks. We also discuss that the thermal parameters soft sensor is of importance for economic running in power plants.
出处
《武汉大学学报（工学版）》
CAS
CSCD
北大核心
2005年第2期99-101,共3页
Engineering Journal of Wuhan University
基金
"十五"国家科技攻关计划资助项目(2001 BA201A04).
关键词
支持向量机
径向基神经网络
热工过程
软仪表
经济性监测
support vector machine (SVM)
radial basis function (RBF) neural networks
thermal power process
soft sensor
economic monitoring
