Exponential synchronization of inertial neural networks with mixed delays via quantized pinning control

Feng, Yuming; Xiong, Xiaolin; Tang, Rongqiang; Yang, Xinsong

doi:10.1016/j.neucom.2018.05.030

Cited by 62 publications

(23 citation statements)

References 40 publications

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“…T ∈ R n denotes the state of the ith CINN, Φ ∈ R n×n is an inner coupling matrix and U = (u ij ) N × N is an outer-coupling weight configuration matrix, which is irreducible and satisfies the following condition: (38). The CINNs (2) is said to be globally exponentially synchronized with (1) if there exist constants > 0, ≥ 1 such that ||x * (t)|| ≤ e − t || || C 1 (t ≥ 0) holds for any initial values.…”

Section: Preliminariesmentioning

confidence: 99%

“…Remark 1 (38). As well known, for a given network with irreducible configuration matrix, one can always rearrange the order of the nodes and take the first l-th nodes to be controlled.…”

Section: Preliminariesmentioning

confidence: 99%

“…Consider the INN (1) with the following parameters 38 : For the pinning controller, we take W 1 = W 2 = … = W l = I 2 . The parameters of the quantizer are chosen as 0 = 50, = 0.8 then = 1/9.…”

Section: Numerical Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Exponential synchronization of coupled inertial neural networks with mixed delays via weighted integral inequalities

Vong

Shi

Yao

2020

Intl J Robust & Nonlinear

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We study the exponential synchronization of coupled inertial neural networks with both discrete-time delay and distributed delay by quantized pinning controllers. Novel integral inequalities, which generalize the Jensen-based inequality, are developed by choosing appropriate weight functions in our recent work. An exponential synchronization criterion is established by applying these inequalities to analyzing a Lyapunov-Krasovskii functional which takes mixed delays into account. Numerical simulations show that the criterion can reduce conservativeness when designing parameters of the controller.

show abstract

Section: Preliminariesmentioning

confidence: 99%

“…Remark 1 (38). As well known, for a given network with irreducible configuration matrix, one can always rearrange the order of the nodes and take the first l-th nodes to be controlled.…”

Section: Preliminariesmentioning

confidence: 99%

See 1 more Smart Citation

Exponential synchronization of coupled inertial neural networks with mixed delays via weighted integral inequalities

Vong

Shi

Yao

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…They have extensive applications in various fields, such as power engineering and secure communication [1][2][3][4][5][6]. Therein, synchronization, as one of the most important dynamic characteristics of coupled networks, plays a significant role in many fields such as biological systems, chemical reactions, and information technology [7][8][9][10][11][12][13][14]. As is well known, when coupled networks are not synchronized by themselves, some controllers are always designed to ensure the synchronization of coupled networks.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of stochastic multiple weighted coupled networks with Markovian switching

2020

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We investigate the synchronization of stochastic multiple weighted coupled networks with Markovian switching (SMWCNMS). By designing an appropriate controller, we obtain several sufficient criteria ensuring the pth moment exponential synchronization and almost surely exponential synchronization for SMWCNMS based on graph theory. Moreover, we also investigate the pth moment asymptotical synchronization and almost surely asymptotical synchronization for SMWCNMS. Finally, we provide a numerical example to illustrate the availability of the proposed synchronization criteria.

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“…In the past few years, various synchronization problems have been investigated in previous works, such as special module‐phase synchronization, outer synchronization,( local synchronization, global exponential synchronization, stochastic synchronization, cluster synchronization, projective synchronization, and so on. In order to drive the network to synchronize, many effective control techniques have been reported, such as the activation control, the linear separation method, the linear coupling method, state‐feedback control, adaptive control, event‐triggered control, sampled‐data control, impulsive control, intermittent control, sliding mode control, and pinning control . However, compared with this control methods, pinning control is an effective and practical method for achieving pinning synchronization of complex cyberphysical networks, as it has the following advantages: (1) only a small fraction of nodes need to be controlled and (2) it not only saves the energy but also reduces control cost if the network size N is very large.…”

Section: Introductionmentioning

confidence: 99%

Robust pinning synchronization of complex cyberphysical networks under mixed attack strategies

Song

Liu

Tan

2018

Intl J Robust & Nonlinear

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This paper is concerned with the robust pinning synchronization control problem in complex cyberphysical networks under mixed attacks. The channels for transmitting control input signals and receiving information agent are assumed to be independent of each other and both can be destroyed by frequently occurred attacks. After the attacks, a repairing cell will be instantly activated until the communication topology is restored to the initial state.Assume that broken-down nodes remain partial function. Moreover, feedback gain matrices and coupling strengths will be designed by some algorithms. By dint of geometrical method and inequality technique, sufficient conditions are established to guarantee robust synchronization for the complex cyberphysical networks. Finally, a simulation example is utilized to illustrate the obtained results.KEYWORDS attack, complex cyberphysical networks, pinning control, robust synchronization INTRODUCTIONMany complex systems can be modeled as a kind of complex networks in nature and society. Classic example includes metabolic networks of cells, food webs, public transportation networks of air traffic, social networking services, and so on. 1,2 A complex network exists plenty of nodes and edges where nodes imitate the entity of these systems and edges represent the interactions among them. Over the last couple of decades, the research of complex cyberphysical networks has witnessed trending topics due to advances in computing, communication, and related hardware technologies. Complex cyberphysical network refers to a new generation of complex networks whose normal functioning significantly relies on tight integration of embedded computation, networks, and controlled physical processes. The integration does not mean the simple convergence of the physical world and the cyberspace, but the deep interaction of all the physical and cybercomponents such as Internet of Things, water service systems, power systems, transportation systems, and many other areas that are directly related to people's daily lives. 3,4 Meanwhile, cyberphysical networks need to guarantee the execution of multiple control policies and communications with sensors and actuators at a suitable rate and optimize the performance of control, security, and management function. 5 For the immense field of application of cyberphysical networks, various problems have been considered, such as observer design, 6 robustness analysis, 7 security, 8,9 and resilience problems. 10 Especially, physical components affect communication and computation, while embedded computers in networks likely bring some security problem during monitor and control the physical elements. Thus, the high reliability of system security in cyberphysical systems is challenging and important in practical application. Only with improved stability Int J Robust Nonlinear Control. 2019;29:1265-1278.wileyonlinelibrary.com/journal/rnc

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Exponential synchronization of inertial neural networks with mixed delays via quantized pinning control

Cited by 62 publications

References 40 publications

Exponential synchronization of coupled inertial neural networks with mixed delays via weighted integral inequalities

Exponential synchronization of coupled inertial neural networks with mixed delays via weighted integral inequalities

Synchronization of stochastic multiple weighted coupled networks with Markovian switching

Robust pinning synchronization of complex cyberphysical networks under mixed attack strategies

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