Global Lagrange stability for neutral-type inertial neural networks with discrete and distributed time delays. (English) Zbl 07832473
Summary: This paper focus on the problem of global Lagrange stability for neutral-type inertial neural networks with discrete and distributed time delays. By choosing a proper variable substitution, an inertial neural network consisting of second-order differential equations can be converted into a first-order differential model. The sufficient conditions of the inertial neural network with neutral delay are derived by constructing suitable Lyapunov-Krasovskii functional candidates, introducing new free weighting matrices, utilizing inequality techniques and analytical method. Through the LMI condition, we analyze the global exponential stability of the delayed inertial neural networks in Lagrange sense. Meanwhile, the global exponential attractive set is also given. Finally, some example is given to illustrate our theoretical results.
MSC:
| 34Kxx | Functional-differential equations (including equations with delayed, advanced or state-dependent argument) |
| 92Bxx | Mathematical biology in general |
| 93Dxx | Stability of control systems |
Keywords:
inertial neural networks; Lyapunov-Krasovski functional; Lagrange stability; neutral delay; linear matrix inequality; global exponential attractive setReferences:
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