New results on passivity analysis of uncertain neural networks with time-varying delays

Abstract

In this paper, the passivity problem is investigated for a class of uncertain neural networks with generalized activation functions. By employing an appropriate Lyapunov–Krasovskii functional, a new delay-dependent criterion for the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.

Keywords:

• uncertain neural networks
• time-varying delays
• passivity
• Lyapunov–Krasovskii functional
• linear matrix inequality

2000 AMS Subject Classification :

• 92B20
• 15A06
• 15A39
• 37FXX
• 37NXX

Acknowledgements

The authors would like to thank the reviewers and the editor for their valuable suggestions and comments which have led to a much improved paper. This work was supported in part by the National Natural Science Foundation of China under grants 10772152, and the Scientific Research Fund of Chongqing Municipal Education Commission under Grant KJ070401, an International Joint Project sponsored by the Royal Society of the UK and the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany.