摘要: |
|
关键词: |
DOI: |
Received:September 13, 2008Revised:July 28, 2009 |
基金项目: |
|
Relaxed delay-dependent exponential stability condition for a class of neural networks with polytopic uncertainties and distributed delays |
Jianwei XIA,Hongbin ZHANG |
(School of Automation, Southeast University; School of Mathematics Science, Liaocheng University;School of Electronic Engineering, University of Electronic Science and Technology) |
Abstract: |
The global robust exponential stability of a class of neural networks with polytopic uncertainties and distributed delays is investigated in this paper. Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability of the equilibrium point of the considered neural networks. The derived sufficient condition is proposed in terms of a set of relaxed linear matrix inequalities (LMIs), which can be checked easily by recently developed algorithms solving LMIs. A numerical example is given to demonstrate the effectiveness of the proposed criteria. |
Key words: Neural networks Polytopic uncertainties Distributed delays Delay-dependent Global robust exponential stability |