Edge-Based Fractional-Order Adaptive Strategies for Synchronization of Fractional-Order Coupled Networks With Reaction–Diffusion Terms

Lv, Yujiao; Hu, Cheng; Yu, Juan; Jiang, Haijun; Huang, Tingwen

doi:10.1109/tcyb.2018.2879935

Cited by 100 publications

(20 citation statements)

References 63 publications

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“…Thus, it is necessary to investigate the coupled reaction-diffusion neural networks (CRDNNs). Recently, many meaningful results on the synchronization of CRDNNs have been obtained [14][15][16][17][18]. In [14], the authors established some synchronization conditions for CRDNNs.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered passivity of multi-weighted coupled delayed reaction-diffusion memristive neural networks with fixed and switching topologies

Huang

Lin

Yang

2020

Communications in Nonlinear Science and Numerical Simulation

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This paper solves the event-triggered passivity problem for multiple-weighted coupled delayed reaction-diffusion memristive neural networks (MWCDRD-MNNs) with fixed and switching topologies. On the one side, by designing appropriate event-triggered controllers, several passivity criteria for MWC-DRDMNNs with fixed topology are derived based on the Lyapunov functional method and some inequality techniques. Moreover, some adequate conditions for ensuring asymptotical stability of the event-triggered passive network are presented. On the other side, we take the switching topology in network model into consideration, and investigate the event-triggered passivity and passivity-based synchronization for MWCDRDMNNs with switching topology. Finally, two examples with numerical simulation results are provided to illustrate the effectiveness of the obtained theoretical results.

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Section: Introductionmentioning

confidence: 99%

Event-triggered passivity of multi-weighted coupled delayed reaction-diffusion memristive neural networks with fixed and switching topologies

Huang

Lin

Yang

2020

Communications in Nonlinear Science and Numerical Simulation

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“…satisfying (36) and (37). According to eorem 3, the MMDNNs (12) under the impulsive controller (9) are outputstrictly passive.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…But multiple NNs can solve some difficult problems through cooperation with each other so that the cost can be reduced. Recently, some cooperative control problems [32][33][34][35] involving passivity and synchronization [36][37][38] have been concerned in multiple NNs. Unfortunately, as far as we know, the passivity and synchronization of multiple multi-delayed NNs (MMDNNs) via impulsive control have never been considered.…”

Section: Introductionmentioning

confidence: 99%

Passivity and Synchronization of Multiple Multi-Delayed Neural Networks via Impulsive Control

Yong

Yang

Liu

et al. 2020

Discrete Dynamics in Nature and Society

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This paper is concerned with the passivity and synchronization for multiple multi-delayed neural networks (MMDNNs) under impulsive control. To ensure the passivity, input strict passivity, and output strict passivity in MMDNNs, a suitable impulsive controller is designed. Moreover, an impulsive time-dependent Lyapunov functional is exploited to obtain the synchronization criterion of MMDNNs, where the criterion is formulated by linear matrix inequalities. Numerical examples are given to verify the validity of the theoretical results.

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“…The environment of real‐world dynamical networks always suffer from the external disturbances, which potentially lead to the sudden changes of states and can be modeled as switched systems . The switched systems consist of a finite number of subsystems and a logical rule regulating the switching behaviors among them called switching signal.…”

Section: Introductionmentioning

confidence: 99%

“…The environment of real-world dynamical networks always suffer from the external disturbances, which potentially lead to the sudden changes of states and can be modeled as switched systems. [1][2][3] The switched systems consist of a finite number of subsystems and a logical rule regulating the switching behaviors among them called switching signal. During the past decades, many researchers paid their attention on switched systems due to its extensive applications in power electronics, 4,5 formation control systems, 6 complex dynamical networks with failures, 7 networked control systems, 8 and so on.…”

Section: Introductionmentioning

confidence: 99%

Stability of switched systems with limiting average dwell time

Wang

Lou

2019

Intl J Robust & Nonlinear

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Summary In this paper, the stability problems of a class of switched systems with limiting average dwell time (ADT) are concerned. The common ADT is improved to a form of limit, and the limiting ADT even can be infinite. Different from previous results, in order to take full advantage of stabilizing switchings, switching‐dependent switched parameters are first used to describe the relationship of two consecutive activated switchings. Then, stability criteria of switched systems with limiting ADT are established, which are less conservative comparing with the existing results. Additionally, some stability criteria of switched systems including continuous‐time and discrete‐time cases are derived. Finally, the validity and effectiveness of our results are elucidated by numerical examples.

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Edge-Based Fractional-Order Adaptive Strategies for Synchronization of Fractional-Order Coupled Networks With Reaction–Diffusion Terms

Cited by 100 publications

References 63 publications

Event-triggered passivity of multi-weighted coupled delayed reaction-diffusion memristive neural networks with fixed and switching topologies

Event-triggered passivity of multi-weighted coupled delayed reaction-diffusion memristive neural networks with fixed and switching topologies

Passivity and Synchronization of Multiple Multi-Delayed Neural Networks via Impulsive Control

Stability of switched systems with limiting average dwell time

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