Stochastic synchronization of coupled neural networks with intermittent control. (English) Zbl 1233.34020
Summary: The authors study the exponential stochastic synchronization problem for coupled neural networks with stochastic noise perturbations. Based on Lyapunov stability theory, inequality techniques, the properties of Wiener processes, and by adding different intermittent controllers, several sufficient conditions are obtained to ensure exponential stochastic synchronization of coupled neural networks with or without coupling delays under stochastic perturbations. These stochastic synchronization criteria are expressed in terms of several lower-dimensional linear matrix inequalities (LMIs) and can be easily verified. Moreover, the results of this letter are applicable to both directed and undirected weighted networks. A numerical example and its simulations are offered to show the effectiveness of our new results.
MSC:
| 34D06 | Synchronization of solutions to ordinary differential equations |
| 34C28 | Complex behavior and chaotic systems of ordinary differential equations |
| 34H10 | Chaos control for problems involving ordinary differential equations |
| 34K50 | Stochastic functional-differential equations |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
Keywords:
synchronization; stochastic perturbation; intermittent control; chaotic delayed neural networks; coupled neural networks; asymmetric coupling; coupling delaysReferences:
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