Research on power plant superheated steam temperature based on least squares support vector machines

被引：0
|
作者：
Wang, Yong [1 ,2 ]
Liu, Jizhen [1 ]
Liu, Xiangjie [1 ]
Tan, Wen [1 ]
机构：
[1] North China Elect Power Univ, Dept Automat, Beijing 102206, Peoples R China
[2] ChangChun Inst Technol, Changchun 130012, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
support vector machine; RBF; pruning; Sparse; least square;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the strong nonlinearity and large time-varying characteristics in controlling of super-heater temperature in plant, the method of LS-SVMs based on radial basis function are used to model. Under the condition of modeling approximating to performance, the sparse modeling is gotten by the pruning algorithm. The merits of the algorithm are conforming to the least structural risk in training process and hardly leading to over-fitting. The simulation of a superheating system, in one super critical 600MW direct boiler in one power plant, is taken. The result shows that the controlling system can be adapt to the variation of the object characteristic well with strong nonlinearity and large time-varying characteristics rapidly.
引用
收藏
页码：4752 / +
页数：2
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