Global exponential convergence of delayed inertial Cohen-Grossberg neural networks. (English) Zbl 1529.34067
Summary: In this paper, the exponential convergence of delayed inertial Cohen-Grossberg neural networks (CGNNs) is studied. Two methods are adopted to discuss the inertial CGNNs, one is expressed as two first-order differential equations by selecting a variable substitution, and the other does not change the order of the system based on the nonreduced-order method. By establishing appropriate Lyapunov function and using inequality techniques, sufficient conditions are obtained to ensure that the discussed model converges exponentially to a ball with the prespecified convergence rate. Finally, two simulation examples are proposed to illustrate the validity of the theorem results.
MSC:
| 34K25 | Asymptotic theory of functional-differential equations |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
Keywords:
inertial Cohen-Grossberg neural networks; time-varying delays; exponential convergence; convergence rateReferences:
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