Synchronization of hybrid coupled reaction–diffusion neural networks with time delays via generalized intermittent control with spacial sampled-data

Lu, Binglong; Jiang, Haijun; Hu, Cheng; Abdurahman, Abdujelil

doi:10.1016/j.neunet.2018.04.017

Cited by 58 publications

(27 citation statements)

References 43 publications

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“…For example, in [30], by time sampled-data control, the exponential synchronization problem has been studied for RDNNs with sampled-data communications. In [31], by spatial sampled-data control, the exponential synchronization of RDNNs with time delays has been investigated. In [33], by proposing a time sampleddata controller and a discontinuous LKF, synchronization criteria have been established for RDNNs with time delays.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sampled-Data Control for Synchronization of T–S Fuzzy Reaction–Diffusion Neural Networks With Additive Time-Varying Delays

Zhang

Zeng

Park

et al. 2021

IEEE Trans. Cybern.

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This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Sampled-Data Control for Synchronization of T–S Fuzzy Reaction–Diffusion Neural Networks With Additive Time-Varying Delays

Zhang

Zeng

Park

et al. 2021

IEEE Trans. Cybern.

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“…NNs are easy to physically implement and hence have promising applications especially in secure communications based on synchronization [3,4]. In the past years, NNs synchronization problems are hot topic and have been successfully used to many areas [4,[22][23][24][25][26][27]. In fact, some natural systems can synchronize by themselves, but others cannot attain synchronization by themselves.…”

Section: Introductionmentioning

confidence: 99%

“…In general, master NNs and slave NNs start to adjust from different initial state via the proper control strategy. Some control methods have been applied to obtain synchronization of NNs, such as pinning control [2], decentralized event-triggered control [4], intermittent control [23], sampled-data control [25] , adaptive learning control [26], impulsive control [27,28], and so on. In [25], the exponential synchronization for a class of hybrid coupled delayed RDNNs was considered under intermittent control with spacial sampled-data.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered synchronization of uncertain delayed generalized RDNNs

Zhang

Xing

et al. 2021

Soft Comput

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This paper investigates the exponential synchronization analysis of master-slave chaotic uncertain delayed generalized reaction-diffusion neural networks (GRDNNs) with event-triggered control scheme. A delay GRDNNs system model for the analysis is constructed by investigating the effect of the network transmission delay. By constructing a novel Lyapunov-Krasovskii functional and using a delay system approach for designing event-triggered controllers and some inequality techniques like Jensen's inequality, Wirtinger's inequality and Halanay's inequality, the criteria are obtained for the event-triggered synchronization analysis and control synthesis of delayed GRDNNs. The synchronization criteria are formulated in terms of linear matrix inequalities. Finally, we conclude that the slave systems synchronize with the master systems. Two examples show the proposed theoretical results are feasible and effective.

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“…2. In some literature, 27,28,32,33 the disturbances of the master-slave networks are regarded as constant disturbances, which are directly eliminated when the error system is constructed. In order to make the models more realistic in this article, stochastic disturbances and time-varying delays are both considered in RDNNs, which is more complex than the idealized network models, but makes the system description more accurate.…”

Section: Introductionmentioning

confidence: 99%

Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

Song

Ning

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The synchronization of reaction-diffusion neural networks with state and spatial couplings is investigated in this article, and the time-varying delay and stochastic disturbances are considered in the proposed systems. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Here, we suggest a spatial sampled-data controller design, where the sampled-data (in space) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in space are bounded. Based on the Lyapunov stability theory, Young’s and Wirtinger’s inequalities techniques, some sufficient conditions are presented to synchronize the hybrid coupling reaction-diffusion neural networks with stochastic disturbances. Finally, the efficiency of the derived criteria will be demonstrated by resorting to two numerical examples.

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Synchronization of hybrid coupled reaction–diffusion neural networks with time delays via generalized intermittent control with spacial sampled-data

Cited by 58 publications

References 43 publications

Fuzzy Sampled-Data Control for Synchronization of T–S Fuzzy Reaction–Diffusion Neural Networks With Additive Time-Varying Delays

Fuzzy Sampled-Data Control for Synchronization of T–S Fuzzy Reaction–Diffusion Neural Networks With Additive Time-Varying Delays

Event-triggered synchronization of uncertain delayed generalized RDNNs

Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

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