A method for stability analysis of nonlinear discrete-time systems

被引：21
|
作者：
Barabanov, NE [1 ]
Prokhorov, DV
机构：
[1] N Dakota State Univ, Dept Math, Fargo, ND 58102 USA
[2] Ford Res Lab, Grp Artificial Neural Syst & Fuzzy Log, Dearborn, MI 48121 USA
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2003年 / 48卷 / 12期
关键词：
discrete-time recurrent neural network; exponential stability; lyapunov stability; RMLP; recurrent neural networks (RNNs); sector monotone nonlinearity;
D O I：
10.1109/TAC.2003.820158
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We address the problem of global Lyapunov stability of discrete-time systems with known coefficients. We develop a method for reduction of dissipativity domain effectively testing if the system has a convex Lyapunov function. Our implementation is immediately applicable to differentiable systems with bounded nonlinearities, but the method proposed is more general and applicable to nondifferentiable systems with bounded right-hand sides. Our main application emphasis is on stability analysis of recurrent neural networks. We illustrate how to use our approach with examples.
引用
收藏
页码：2250 / 2255
页数：6
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