Stability analysis of discrete-time recurrent multilayer neural networks

被引：0

作者：

Barabanov, NE ^{[1
]}

Prokhorov, DV ^{[1
]}

机构：

[1] N Dakota State Univ, Fargo, ND 58105 USA

来源：

2004 43RD IEEE CONFERENCE ON DECISION AND CONTROL (CDC), VOLS 1-5 | 2004年

关键词：

D O I：

10.1109/CDC.2004.1429592

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 recurrent multilayer neural networks (RMLNN) in the unforced (unperturbed) setting. It is assumed that network weights are fixed to some values, for example, those attained after training. To apply the method of reduction of attractor estimate. we use the state space extension method to present RMLNN in the form of discrete-time dynamical system. We describe also a new algorithm for checking the global asymptotic stability of RMLNN, which is also based on the method of reduction of attractor estimate, and is much better from the computational viewpoint. An example shows the efficiency of this new algorithm.

引用

页码：4958 / 4963

页数：6

共 5 条

[1] A method for stability analysis of nonlinear discrete-time systems [J].