Exponential Stabilization of Delayed Inertial Memristive Neural Networks via Aperiodically Intermittent Control Strategy
Dan Liu, Dan Ye
Abstract
This article is concentrated on the stabilization problem of inertial memristive neural networks (IMNNs) with time-varying delay. Here, removing the reduced order method, the stabilization of delayed IMNNs is directly studied under the framework of the second-order system. To decrease control cost, the aperiodically intermittent control strategy is adopted. This strategy implies that work time (with control input) alternates with rest time (without control input), and every work or rest width may be different. By constructing a suitable Lyapunov functional, some algebraic criteria are achieved to guarantee the stabilization of the considered system. Note that the obtained control gains are often larger than practical needs due to the conservatism deriving from inevitable inequality scaling. For further saving control resources, an intermittent adaptive control strategy is applied to stabilize the delayed IMNNs. Finally, a numerical example is exhibited to confirm the availability of the developed theoretical outcomes.