Pinning synchronization of delayed neural networks

Zhou, Jin; Wu, Xiaoqun; Yu, Wenwu; Small, Michael; Lu, Jun-an

doi:10.1063/1.2995852

Cited by 77 publications

(49 citation statements)

References 21 publications

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“…[1][2][3][4][5] In particular, special attention has been focused on the control and synchronization of largescale complex dynamical networks with certain types of topology. [6][7][8][9][10][11][12][13][14][15] Another attractive topic on complex networks is to develop systematic schemes for the topology identification. Applications can be found in various scientific and engineering fields.…”

Section: Introductionmentioning

confidence: 99%

Topology identification of complex dynamical networks

Zhao

et al. 2010

Chaos

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Recently, some researchers investigated the topology identification for complex networks via LaSalle's invariance principle. The principle cannot be directly applied to time-varying systems since the positive limit sets are generally not invariant. In this paper, we study the topology identification problem for a class of weighted complex networks with time-varying node systems. Adaptive identification laws are proposed to estimate the coupling parameters of the networks with and without communication delays. We prove that the asymptotic identification is ensured by a persistently exciting condition. Numerical simulations are given to demonstrate the effectiveness of the proposed approach. © 2010 American Institute of Physics. ͓doi:10.1063/1.3421947͔The topology identification, as an inverse problem, is a significant issue in the study of complex networks. For example, if a major malfunction occurs in a communication network, power network, or the Internet, it is very important to quickly detect the location of the faulty line. This paper proposes a novel adaptive identification approach for the topology identification of the weighted complex dynamical networks. We show that the concept of persistent excitation plays a key role in the process of topology identification. Our result overcomes the limitation of previous methods, which rely on the use of LaSalle's invariance principle, and is applicable to networks with time-varying node systems and diverse time-varying coupling delays.

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Section: Introductionmentioning

confidence: 99%

Topology identification of complex dynamical networks

Zhao

et al. 2010

Chaos

Self Cite

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“…which is a positive constant, and others are 0, then for a symmetric matrix G which has the same dimension as matrix M the following two conditions are equivalent when m is large enough [14]:…”

Section: Lemma 1 the Following Linear Matrix Inequality (Lmi)mentioning

confidence: 95%

“…and K is a diagonal matrix whose i k th elements are a and the others are 0. h Provided with (14) in Theorem 3, we have…”

Section: Construct a Lyapunov Candidatementioning

confidence: 97%

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Pinning a stochastic neural network to the synchronous state

Peng

Lei

2010

Neural Comput & Applic

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In this paper, the asymptotic stability of the pinning synchronous solution of stochastic neural networks with and without time-delays is analyzed. The delays are time-varying, and the uncertainties are norm-bounded that enter into all the parameters of network and control. The aim of this paper is not only to establish easily verifiable conditions under which the pinning synchronous solution of stochastic neural network is globally asymptotically stable but also to give a feasible way to offset the limitation of network itself in order to reach synchronization. In addition, a specific neurobiological network is also introduced, and some numerical examples are provided to illustrate the applicability of the proposed criteria.

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“…Such as, based on adaptive pinning control, the synchronization problems were investigated in Zhou et al (2008), for a general weighted neural network with coupling delay. Cluster synchronization problem was studied in Li and Cao (2011) for an array of coupled stochastic delayed neural networks by using pinning control strategy.…”

Section: Introductionmentioning

confidence: 99%

Pinning synchronization of coupled inertial delayed neural networks

Cao

Alofi

et al. 2014

Cogn Neurodyn

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The paper is devoted to the investigation of synchronization for an array of linearly and diffusively coupled inertial delayed neural networks (DNNs). By placing feedback control on a small fraction of network nodes, the entire coupled DNNs can be synchronized to a common objective trajectory asymptotically. Two different analysis methods, including matrix measure strategy and Lyapunov-Krasovskii function approach, are employed to provide sufficient criteria for the synchronization control problem. Comparisons of these two techniques are given at the end of the paper. Finally, an illustrative example is provided to show the effectiveness of the obtained theoretical results.

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Pinning synchronization of delayed neural networks

Cited by 77 publications

References 21 publications

Topology identification of complex dynamical networks

Topology identification of complex dynamical networks

Pinning a stochastic neural network to the synchronous state

Pinning synchronization of coupled inertial delayed neural networks

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