Finite-time and fixed-time stabilization of inertial memristive Cohen-Grossberg neural networks via non-reduced order method. (English) Zbl 1484.93016
Summary: In this paper, we focus on the finite-time and fixed-time stabilization of inertial memristive Cohen-Grossberg neural networks. To cope with the effect caused by inertial (second-order) term, most of the previous literature use the variable translation to reduce the order. Different from that, by directly designing a Lyapunov functional and feedback controller, a novel non-reduced order method is proposed in this paper to solve the finite-time (fixed-time) stabilization problem of inertial memristive Cohen-Grossberg neural networks. Two kinds of time delays are considered in our network model, novel criteria are then derived for both cases. Lastly, numerical examples are given to verify the validity of the theoretical results.
MSC:
| 93D15 | Stabilization of systems by feedback |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 93D40 | Finite-time stability |
| 93A14 | Decentralized systems |
Keywords:
Cohen-Grossberg neural networks; inertial term; mixed time delays; finite-time (fixed-time) stabilizationReferences:
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