<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.gif" display="inline" overflow="scroll"><mml:mi>p</mml:mi></mml:math>th moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control

Yang, Xinsong; Cao, Jinde; Qiu, Jianlong

doi:10.1016/j.neunet.2015.01.008

Cited by 142 publications

(9 citation statements)

References 40 publications

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“…At the same time, the adverse effects of parameter mismatch can also be eliminated by the adaptive controller. Then, different from the existing results on mean square synchronization 43,44 and pth moment exponential synchronization, 45 almost surely exponential synchronization is obtained via impulsive control in this paper. And Zeno behavior can be excluded in almost sure sense.…”

Section: Introductionmentioning

confidence: 66%

Synchronization of stochastic memristive neural networks via event‐triggered impulsive control

Zhang,

Mu,

2023

Adaptive Control & Signal

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This paper investigates the synchronization problem for master‐slave stochastic memristive neural networks (SMNNs) with time‐delay via event‐triggered impulsive control (ETIC). Firstly, a novel ETIC method is designed for SMNNs to realize quasi‐synchronization in mean square sense. Different from time‐triggered impulsive control (TTIC) in the existing results on SMNNs, the proposed event‐triggered method determines impulsive instants based on the real‐time system states, which can reduce redundant impulses. Compared with the existing ETIC for deterministic memristive neural networks (MNNs), the proposed event‐triggered mechanism (ETM) can exclude Zeno behavior completely by adding the waiting time, when stochastic disturbances occur in MNNs. Secondly, if the master SMNNs have nonzero control inputs, an adaptive‐impulsive hybrid controller with ETM is designed to realize almost surely exponential synchronization. Under the ETM, the number of transmitted events is reduced, then control resources can be saved. Zeno behavior can be excluded in almost sure sense. Ultimately, simulations are presented to illustrate the validity of the obtained results.

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Section: Introductionmentioning

confidence: 66%

Synchronization of stochastic memristive neural networks via event‐triggered impulsive control

Zhang,

Mu,

2023

Adaptive Control & Signal

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“…Only the quasi-synchronization of MNNs can be guarantee by utilizing ETIC methods [37][38][39]. Inspired by [16,17,30], we adopt the sign term −k 2 sign(e(t k )) to compensate the mismatched terms, which is not used in existing event-triggered hybrid impulsive controllers [38,39]. Moreover, the proposed event-triggered hybrid state feedback and impulsive control method can also be used to study the complete synchronization of heterogenous dynamic networks [13].…”

Section: Resultsmentioning

confidence: 99%

“…Note that the mathematical model description provided here is similar to common impulsive systems. However, differing from impulsive time sequences, which are periodic in [15][16][17][18][19], the impulsive time sequences herein are event-triggering instants determined by the predefined event-triggered condition. Therefore, the communication burden can be reduced, and limited communication resources can be utilized effectively.…”

Section: Definition 1 ([40]mentioning

confidence: 99%

“…Besides, it only requires small control gains and acts at certain discrete instants, thereby reducing the amount of transmitted information and control costs [14]. Considering the advantages of the impulsive control strategy, the issue of impulsive synchronization of MNNs has been wildly studied [15][16][17][18][19]. In [15], by utilizing the distributed impulsive control method, the synchronization problem for a class of MNNs with stochastic disturbance has been extensively studied.…”

Section: Introductionmentioning

confidence: 99%

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Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Zhang

Bao

2020

Sci. China Inf. Sci.

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This paper is concerned with the complete synchronization of memristive neural networks (MNNs) with time-varying delays. An event-triggered hybrid state feedback and impulsive controller is designed to save the limited system communication resources, and parameter mismatch is considered in the control design process. Based on the Lyapunov functional approach and the comparison principle for impulsive systems, a sufficient synchronization criterion is developed to derive the master MNN and response MNN. Additionally, under the event-triggered mechanism there exists a positive lower bound for inter-execution time, which implies the avoidance of Zeno behavior. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed synchronization design methods.

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“…Moreover, there are contributions on CS of NNs where memristors are modeled as elements switching between two values of the resistance displayed by the pinched hysteresis loop [43], [44]. We also mention the related relevant papers [45], [46] and [47] dealing with techniques for synchronization of various classes of NNs with delay and switching memristors. However, how to employ such switching models to describe real memristor devices has not been studied so far.…”

Section: Introductionmentioning

confidence: 99%

Complete Stability of Neural Networks With Extended Memristors

Marco

Forti

Moretti

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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The article considers a large class of delayed neural networks (NNs) with extended memristors obeying the Stanford model. This is a widely used and popular model that accurately describes the switching dynamics of real nonvolatile memristor devices implemented in nanotechnology. The article studies via the Lyapunov method complete stability (CS), i.e., convergence of trajectories in the presence of multiple equilibrium points (EPs), for delayed NNs with Stanford memristors. The obtained conditions for CS are robust with respect to variations of the interconnections and they hold for any value of the concentrated delay. Moreover, they can be checked either numerically, via a linear matrix inequality (LMI), or analytically, via the concept of Lyapunov diagonally stable (LDS) matrices. The conditions ensure that at the end of the transient capacitor voltages and NN power vanish. In turn, this leads to advantages in terms of power consumption. This notwithstanding, the nonvolatile memristors can retain the result of computation in accordance with the in-memory computing principle. The results are verified and illustrated via numerical simulations. From a methodological viewpoint, the article faces new challenges to prove CS since due to the presence of nonvolatile memristors the NNs possess a continuum of nonisolated EPs. Also, for physical reasons, the memristor state variables are constrained to lie in some given intervals so that the dynamics of the NNs need to be modeled via a class of differential inclusions named differential variational inequalities.

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pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control

Cited by 142 publications

References 40 publications

Synchronization of stochastic memristive neural networks via event‐triggered impulsive control

Synchronization of stochastic memristive neural networks via event‐triggered impulsive control

Event-triggered hybrid impulsive control for synchronization of memristive neural networks

Complete Stability of Neural Networks With Extended Memristors

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