Finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delay and application

Hui, Meng; Zhang, JiaHuang; Yao, Ning; Wu, Weizhe

doi:10.1007/s00521-023-08474-4

Cited by 5 publications

References 48 publications

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Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Zhang,

Wei,

Wang

et al. 2024

Adaptive Control & Signal

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SummaryThe cluster synchronization problem of stochastic two‐layer networks with infinite distributed delays is concerned. Firstly, we study the cluster synchronization of the first layer (leader‐layer) network with the average state of each cluster of sets as synchronization target. Secondly, we design a delayed pinning impulsive controller to synchronize the second layer (follower‐layer) network to the first layer network in a mean square cluster sense. Based on stochastic impulsive analysis and Lyapunov stability theory, some sufficient conditions for cluster synchronization are obtained. Finally, the effectiveness of the theoretical results is verified through numerical simulations.

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Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Zhang,

Wei,

Wang

et al. 2024

Adaptive Control & Signal

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H∞$$ {H}_{\infty } $$ Synchronization of Semi‐Markovian Switching Complex‐Valued Networks With Time‐Varying Delay: A Delay‐Dependent Event‐Triggered Mechanism

Li,

Wei,

Gong

et al. 2024

Intl J Robust & Nonlinear

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This paper focuses on the synchronization issue for delayed semi‐Markovian switching complex‐valued networks via the delay‐dependent event‐triggered mechanism. For the first time, in order to reduce the redundant information transmission and the number of false triggers, a novel event‐triggered mechanism is proposed, which involves the network‐induced time delay and the latest triggered signal. Second, based on the proposed delay‐dependent event‐triggered control scheme, the desired results can be realized. Specifically, by utilizing the stochastic analysis technique and Lyapunov stability theory as well as mixed reciprocally convex inequalities, some sufficient less conservative criteria are achieved to ensure the synchronization error system realize the globally asymptotically mean‐square stability with a pre‐established disturbance performance. Furthermore, the expected controller gain matrices can be explicitly designed by solving some solvable matrix inequalities. At the end of paper, an illustrative example with simulations is presented to demonstrate the feasibility and effectiveness of the proposed delay‐dependent event‐triggered scheme.

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Synchronization Analysis of Complex-Valued Artificial Neural Networks with Distributed Delays in Medical Image Processing

Sriraman,

Samidurai

2024

Smart Healthcare and Machine Learning

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Finite-time anti-synchronization and fixed-time quasi-anti-synchronization for complex-valued neural networks with time-varying delay and application

Cited by 5 publications

References 48 publications

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

Cluster synchronization of stochastic two‐layer networks with infinite distributed delays via delayed pinning impulsive control

H∞$$ {H}_{\infty } $$ Synchronization of Semi‐Markovian Switching Complex‐Valued Networks With Time‐Varying Delay: A Delay‐Dependent Event‐Triggered Mechanism

Synchronization Analysis of Complex-Valued Artificial Neural Networks with Distributed Delays in Medical Image Processing

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