Synchronization issue of coupled neural networks based on flexible impulse control

Xiu, Ruihong; Zhang, Wei; Zhou, Zichuan

doi:10.1016/j.neunet.2022.01.020

Cited by 22 publications

(2 citation statements)

References 42 publications

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“…The symmetry will open up new possibilities for productive application of the methods introduced and developed within the last few years for various types of impulsive systems [20][21][22] and networks [23][24][25]. Another interesting opportunity involves combining methods for discontinuous dynamics with those for synchronization [26,27].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Symmetrical Discontinuous Hopfield Neural Networks with Poisson Stable Rates, Synaptic Connections and Unpredictable Inputs

Akhmet,

Nugayeva,

Seilova

2024

Symmetry

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The purpose of this paper is to study the dynamics of Hopfield neural networks with impulsive effects, focusing on Poisson stable rates, synaptic connections, and unpredictable external inputs. Through the symmetry of impulsive and differential compartments of the model, we follow and extend the principal dynamical ideas of the founder. Specifically, the research delves into the phenomena of unpredictability and Poisson stability, which have been examined in previous studies relating to models of continuous and discontinuous neural networks with constant components. We extend the analysis to discontinuous models characterized by variable impulsive actions and structural ingredients. The method of included intervals based on the B-topology is employed to investigate the networks. It is a novel approach that addresses the unique challenges posed by the sophisticated recurrence.

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Section: Introductionmentioning

confidence: 99%

Dynamics of Symmetrical Discontinuous Hopfield Neural Networks with Poisson Stable Rates, Synaptic Connections and Unpredictable Inputs

Akhmet,

Nugayeva,

Seilova

2024

Symmetry

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“…According to the diversity of characteristics of collective dynamics behavior, synchronization can be classified into complete synchronization, cluster synchronization, projection synchronization, phase synchronization, etc [12]- [14], [15]- [28], which usually can be reached by adding external controller. So far, a number of control methods have been proposed, including impulsive control [18], [19]- [25], sampled-date control [26], pinning control [27], adaptive control [15], eventtriggered control [28] and so on. Among them, impulsive control is a very attractive method, which is only activated at some discrete moments.…”

Section: Introductionmentioning

confidence: 99%

Multisynchronization of Delayed Fractional-Order Neural Networks via Average Impulsive Interval

Wang,

Ding,

et al. 2023

Neural Process Lett

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This paper focuses on the multisynchronization problem of delayed fractional-order neural networks with uncertain parameters. Firstly, partition space method is used to determine that each subnetwork of fractional-order neural networks has n j=1 (Kj + 1) locally Mittag-Leffler stable periodic orbits or equilibrium points. Secondly, a universal impulsive controller based on average impulsive interval is proposed to impose on each node except the last one, then sufficient conditions for the dynamical and static multisynchronization of whole systems are given. In addition, it is also proved that the results are compatible for the integer-order networks. Finally, two numerical examples are given to illustrate the correctness of the theoretical results.

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Stability analysis of aperiodic impulsive delay systems using sum of squares approach

Qi,

Sun

2024

Asian Journal of Control

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The stability problem of aperiodic impulsive delay systems with unstable continuous and discrete dynamics is addressed in this article. New delay‐dependent stability conditions are proposed by employing a clock‐dependent Lyapunov–Krasovskii‐like functional composed of a Lyapunov–Krasovskii functional and a high‐order polynomial matrix function. The stability conditions are then solved using sum of squares programming techniques, yielding less conservative results than the looped‐functionals approach. Finally, the simulation results demonstrate the effectiveness of the results.

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Synchronization issue of coupled neural networks based on flexible impulse control

Cited by 22 publications

References 42 publications

Dynamics of Symmetrical Discontinuous Hopfield Neural Networks with Poisson Stable Rates, Synaptic Connections and Unpredictable Inputs

Dynamics of Symmetrical Discontinuous Hopfield Neural Networks with Poisson Stable Rates, Synaptic Connections and Unpredictable Inputs

Multisynchronization of Delayed Fractional-Order Neural Networks via Average Impulsive Interval

Stability analysis of aperiodic impulsive delay systems using sum of squares approach

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