Adaptive Nonlinear Systems Identification via Dynamic Multilayer Neural Networks with Two-Time Scales

被引：0
|
作者：
Fu, Zhi-Jun [1 ]
Xie, W. F. [2 ]
Liu, S. [2 ]
机构：
[1] Zhejiang Univ Technol, Coll Mech Engn, Hangzhou 310014, Zhejiang, Peoples R China
[2] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS (CCA) | 2013年
关键词：
DIFFERENT TIME-SCALES; STABILITY ANALYSIS;
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：
This paper presents a novel adaptive identification method for nonlinear systems including the aspects of fast and slow phenomenon via dynamic multilayer neural networks with two-time scales. The Lyapunov function and singularly perturbed techniques are used to develop the learning procedure for the hidden layers and output layers of the dynamic neural networks model. Novel correction terms are proposed in the learning algorithm to guarantee bounded tracking errors and bounded weights. The effectiveness of the algorithm is illustrated via the simulation results on an electric induction motor.
引用
收藏
页码：1012 / 1017
页数：6
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