RBF neural network and its application in IMC

被引:0
作者
Qu, Yang [1 ]
Xu, Lin [1 ]
Wang, Hanhui [1 ]
Fang, Xiaoke [1 ]
Gu, Shusheng [1 ]
机构
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110004, 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
RBF neural network; IMC; nonlinear system; inverse model; speed control;
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
The IMC controller based on RBF adaptive neural network is designed by the capacity of nonlinear approach of RBF neural network. The problem of nonlinear and uncertainty bound is solute. The weigh coefficient that is difference between expectation output of internal model and output of the object is designed. The recurrence learn algorithm is used to carry out RBF neural network cluster center by SOFM neural network, and recurrence least square is used to fix on weigh of RBF neural network The simulation results show that new method leads to improve dynamic performance and robustness.
引用
收藏
页码:2408 / +
页数:2
相关论文
共 11 条
[1]  
ASRIEL UL, 1993, CONTROL ABILITY STAB, V4, P192
[2]   INTERNAL MODEL CONTROL .1. A UNIFYING REVIEW AND SOME NEW RESULTS [J].
GARCIA, CE ;
MORARI, M .
INDUSTRIAL & ENGINEERING CHEMISTRY PROCESS DESIGN AND DEVELOPMENT, 1982, 21 (02) :308-323
[3]  
GUEZ A, 1998, IEEE CONTROL SYSTEM, V4, P22
[4]  
HANG CC, 1991, IEEE P D, V38, P111
[5]  
KOHONEN T, 1988, IEEE TAB NEURAL NETW
[6]   MODEL ALGORITHMIC CONTROL (MAC) - BASIC THEORETICAL PROPERTIES [J].
ROUHANI, R ;
MEHRA, RK .
AUTOMATICA, 1982, 18 (04) :401-414
[7]  
Wu Yun-jie, 2002, Journal of System Simulation, V14, P1232
[8]  
XU XY, 1999, J CIRCUITS SYSTEMS, V4, P86
[9]  
ZAME G, 1966, IEEE, V12, P228
[10]   FEEDBACK, MINIMAX SENSITIVITY, AND OPTIMAL ROBUSTNESS [J].
ZAMES, G ;
FRANCIS, BA .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1983, 28 (05) :585-601