RBFNN-based multiple steady states controller for nonlinear system and its application

被引：0
|
作者：
Li, XG [1 ]
Huang, DX
Jin, YH
机构：
[1] Tsing Hua Univ, Dept Automat, Beijing 100084, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligent Sci, Beijing 100080, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2005, PT 3, PROCEEDINGS | 2005年 / 3498卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
On-line Radial Basis Function (RBF) neural network based multiple steady states controller for nonlinear system is presented. The unsafe upper steady states can be prevented with the optimizer for Constrained General Model Controller (CGMC). Process simulator package is used to generate a wide range of operation data and the dynamic simulator is built as the real plant. The effectiveness is illustrated with a Continuous Stirred Tank Reactor (CSTR) and OPC tools are developed for on-line data acquisition and computation.
引用
收藏
页码：15 / 20
页数：6
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