Abstract
This paper is concerned with Hopfield neural networks with unbounded time-varying delay driven by G-Brownian motion. The existence and uniqueness of solutions, as well as the continuity of solutions in the sense of G-mean square, are investigated for such neural networks. Moreover, sufficient conditions dependent on delay are derived to guarantee G-mean square asymptotic stability and G-mean square exponential stability of neural networks. At last, two examples are provided to illustrate the application of the obtained results.
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