检索结果-南通市图书馆

New predictive control algorithms based on Least Squares Support Vector Machines

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第5期6卷 440-446页

作者：刘斌苏宏业褚健 National Laboratory of Industrial Control Technology Institute of Advanced Process Control Zhejiang UniversityHangzhou 310027 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan 430081 China

Used for industrial process with different degree of nonlinearity, the two predictive control algorithms presented in this paper are based on Least Squares Support Vector Machines (LS-SVM) model. For the weakly nonlinear system, the system model is built by using LS-SVM with linear kernel function, and then the obtained linear LS-SVM model is transformed into linear input-output relation of the controlled system. However, for the strongly nonlinear system, the off-line model of the controlled system is built by using LS-SVM with Radial Basis function (rbf) kernel. The obtained nonlinear LS-SVM model is linearized at each sampling instant of system running, after which the on-line linear input-output model of the system is built. Based on the obtained linear input-output model, the Generalized Predictive Control (GPC) algorithm is employed to implement predictive control for the controlled plant in both algorithms. The simulation results after the presented algorithms were implemented in two different industrial processes model; respectively revealed the effectiveness and merit of both algorithms.

关键词： Least Squares Support Vector Machines Linear kernel function rbf kernel function Generalized predictive control

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Temperature prediction control based on least squares support vector machines

引用

Control Theory and Technology 2004年第4期 365-370页

作者： Bin LIU, Hongye SU, Weihua HUANG, Jian CHU (National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Yuquan Campus,Hangzhou Zhejiang 310027,China Department of Automation, College of Information Science and Engineering, Wuhan University of Saence and Technology, Wuhan Hubei 430081, China)

A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (rbf) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled *** obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time *** results of the experiment verify the effectiveness and merit of the algorithm.

关键词： Predictive control Least squares support vector machines rbf kernel function Generalized prediction control

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：