Global Asymptotical Stability of Recurrent Neural Networks With Multiple Discrete Delays and Distributed Delays
Jinde Cao, Kun Yuan, Han‐Xiong Li · IEEE Transactions on Neural Networks · 2006
By employing the Lyapunov-Krasovskii functional and linear matrix inequality (LMI) approach, the problem of global asymptotical stability is studied for recurrent neural networks with both discrete time-varying delays and distributed time-varying delays. Some sufficient conditions are given for checking the global asymptotical stability of recurrent neural networks with mixed time-varying delay. The proposed LMI result is computationally efficient as it can be solved numerically using standard commercial software. Two examples are given to show the usefulness of the results.