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Further Results on Fixed-Time Synchronization of the Memristor Neural Networks with Impulsive Effects

Communications in nonlinear science and numerical simulation/Communications in nonlinear science & numerical simulation(2023)

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摘要
The article is mainly concerned with the fixed-time synchronization (FXTS) of memristor neural networks (MNNs) with time-varying delay and impulsive effects. Using the concept of the average impulsive interval and the comparison principle, some sufficient conditions have been derived to achieve the FXTS of MNNs under the effects of synchronizing impulses and desynchronizing impulses. For the case of desynchronizing impulses, the FXTS of MNNs have not been investigated in literature. Moreover, it has been shown through numerical simulation and theoretical derivation that the estimated settling time is less conservative than that of previous results for MNNs under the effects of synchronizing impulses. Finally, a numerical example is provided to illustrate the effectiveness and superiority of the improved synchronization methodology.
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关键词
Fixed-time stability,Finite -time stability,Neural networks,Lyapunov stability
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