A parallel MLPN model with EKF-based on-line learning algorithm

被引：0
|
作者：
Chang, TK [1 ]
Yu, DL [1 ]
Williams, D [1 ]
机构：
[1] Liverpool John Moores Univ, Control Syst Res Grp, Liverpool L3 5UX, Merseyside, England
来源：
CONTROL APPLICATIONS IN MARINE SYSTEMS 2001 (CAMS 2001) | 2002年
关键词：
Learning algorithm; Extended Kalman-filters; process identification; neural networks; time-varying systems;
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：
A parallel multi-layer perceptron network (MLPN) model with on-line learning algorithm is proposed. This parallel MLPN is on-line trained directly in a parallel form. The on-line learning algorithm is based on the Extended Kalman Filter (EKF) algorithm. This parallel MLPN is able to learn the non-linear dynamic behaviour of unknown time-varying systems. The proposed parallel MLPN can be used to model the non-linear systems and perform multi-step-ahead prediction for control purpose. The performance of this model is demonstrated in modelling a multi-variable non-linear continuous stirred tank reactor (CSTR). Copyright 2001 (C) IFAC.
引用
收藏
页码：499 / 504
页数：6
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