首页|Stability analysis of impulsive stochastic delayed Cohen-Grossberg neural networks driven by Lévy noise
Stability analysis of impulsive stochastic delayed Cohen-Grossberg neural networks driven by Lévy noise
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NSTL
Elsevier
? 2022 Elsevier Inc.This note investigates the stabilities for impulsive stochastic delayed Cohen-Grossberg neural networks driven by Lévy noise (ISDCGNNs-LN), including the input-to-state stability (ISS), integral input-to-state stability (iISS) and ?θ(t)-weight input-to-state stability (?θ(t)-weight ISS, θ>0). Utilizing the multiple Lyapunov-Krasovskii (L-K) functions, principle of comparison, constant variation method and average impulsive interval (AII) method, adequate ISS-type stability conditions of the ISDCGNNs-LN under stable impulse and unstable impulse are obtained. This shows that the stochastic systems are ISS in regard to a lower bound of the AII, provided that the continuous stochastic systems is ISS but has destabilizing impulse. Furthermore, the impulse can effectively stabilize the stochastic systems for a upper bound of the AII, provided that the continuous stochastic systems is not ISS. In addition, our results can also deal with the case of variable time delay. In the end, two examples are presented to reflect the rationality and correctness for the theoretical conclusions.
Average impulsive intervalCohen-Grossberg neural networksInput-to-state stabilityLévy noiseTime delay
Yu P.、Deng F.、Sun Y.、Wan F.
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School of Automation Science and Engineering South China University of Technology
NSTL
Elsevier
