Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays 论文
2008IEEE Transactions on Neural Networks引用 344
Neural Networks Stability and SynchronizationNeural Networks and ApplicationsAdvanced Memory and Neural Computing
摘要
In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed in our investigation. The results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applied to recurrent neural networks with constant time delays.