Adaptive robust convergence of neural networks with time-varying delays

This paper is concerned with the adaptive robust convergence for a class of neural networks with time-varying delays. By employing the Lyapunov method and a novel lemma, some delay-independent conditions are derived to guarantee the state variables of the discussed time-varying robust system to converge, globally, uniformly, exponentially to a ball in the state space with a pre-specified convergence rate. Here, the existence and uniqueness of the equilibrium point needs not to be considered. Finally, an illustrated example is given to show the effectiveness and usefulness of the results.