Global exponential asymptotic stability of RNNs with mixed asynchronous time-varying delays
Songfang Jia, Yanheng Chen · Advances in Difference Equations · 2020
Abstract The present article addresses the exponential stability of recurrent neural networks (RNNs) with distributive and discrete asynchronous time-varying delays. Some novel algebraic conditions are obtained to ensure that for the model there exists a unique balance point, and it is global exponential asymptotically stable. Meanwhile, it also reveals the difference about the equilibrium point between systems with and without distributed asynchronous delay. One numerical example and its Matlab software simulations are given to illustrate the correctness of the present results.