Support vector regression-based internal model control  

作　　者：黄宴委 彭铁根 

机构地区：[1]Dept.of Automation,Shanghai Jiaotong University

出　　处：《Journal of Harbin Institute of Technology(New Series)》2007年第3期411-414,共4页哈尔滨工业大学学报（英文版）

摘　　要：This paper proposes a design of internal model control systems for process with delay by using support vector regression(SVR).The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle.Closed-system stability and steady error are analyzed for the existence of modeling errors.The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.This paper proposes a design of internal model control systems for process with delay by using support vector regression （SVR）. The proposed system fully uses the excellent nonlinear estimation performance of SVR with the structural risk minimization principle. Closed-system stability and steady error are analyzed for the existence of modeling errors. The simulations show that the proposed control systems have the better control performance than that by neural networks in the cases of the training samples with small size and noises.

关 键 词：internal model control support vector machine neural networks steady error STABILITY 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]