In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By constructing a new augmented Lyapunov-Krasovskii＇s functional and some novel analysis techniques, improv...In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By constructing a new augmented Lyapunov-Krasovskii＇s functional and some novel analysis techniques, improved delaydependent criteria for checking the stability of the neural networks are established. The proposed criteria are presented in terms of linear matrix inequalities （LMIs） which can be easily solved and checked by various convex optimization algorithms. Two numerical examples are included to show the superiority of our results.展开更多
基金Project supported by the MKE (The Ministry of Knowledge Economy),Koreathe ITRC (Information Technology Research Center) support program supervised by the IITA (Institute for Information Technology Advancement) (Grant No. IITA-2009-C1090-0904-0007)
文摘In this paper, the problem of stability analysis for neural networks with time-varying delays is considered. By constructing a new augmented Lyapunov-Krasovskii＇s functional and some novel analysis techniques, improved delaydependent criteria for checking the stability of the neural networks are established. The proposed criteria are presented in terms of linear matrix inequalities （LMIs） which can be easily solved and checked by various convex optimization algorithms. Two numerical examples are included to show the superiority of our results.
