Event-Triggered Synchronization Strategy for Multiple Neural Networks With Time Delay

Chen, Jiejie; Chen, Boshan; Zeng, Zhigang; Jiang, Ping

doi:10.1109/tcyb.2019.2911029

Cited by 44 publications

(26 citation statements)

References 51 publications

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“…(see Refs. [8][9][10][11][12][13] and the references therein). Accordingly, since Altafini [14] put forward the notion of bipartite consensus mechanism, of which the objective is to drive two antagonistic subnetwork agents to converge to the same modulus state with opposite signs, there has been a surge of boom around this focal topic in MASs, including but not limited to bipartite consensus tracking [15], robust bipartite consensus [16], interventional bipartite consensus [17] and bipartite containment [18].…”

Section: Introductionmentioning

confidence: 99%

Distributed multiple-bipartite consensus in networked Lagrangian systems with cooperative-competitive interactions

Zhang

et al. 2021

Preprint

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In combination with the collective behavior evolutions of bipartite consensus and cluster/group consensus, this paper proposes the notion of multiple-bipartite consensus in networked Lagrangian systems (NLSs). The distributed leaderless and leader-following multiple-bipartite consensus control laws for NLSs are presented in the cooperative-competitive network, where the negative interactions between agents can exist in the same subnetwork. By introducing an acyclic partition and adding the integral item in the control protocols, the final explicit convergence states in the leaderless case are eventually obtained. Moreover, the leader-following scenario can be realized in fifinite time with integrated controllers. All of the effectiveness has been illustrated through numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Distributed multiple-bipartite consensus in networked Lagrangian systems with cooperative-competitive interactions

Zhang

et al. 2021

Preprint

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“…Chen et al [16] discussed the synchronization of multiple neural networks with time delay and proposed the eventtriggered control strategy to guarantee global exponential synchronization under some convenient conditions. The criterion of synchronization is feasible and does not require a Laplacian coupling matrix.…”

Section: Introductionmentioning

confidence: 99%

Distributed Heuristic Adaptive Neural Networks With Variance Reduction in Switching Graphs

Liu

Ding

2021

IEEE Trans. Cybern.

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This article proposes a distributed adaptive training method for neural networks in switching communication graphs to deal with the problems concerned with massive data or privacy-related data. First, the stochastic variance reduced gradient (SVRG) is used for the training of neural networks. Then, the authors propose a heuristic adaptive consensus algorithm for distributed training, which adaptively adjusts the weighted connectivity matrix based on the performance of each agent over the communication graph. Furthermore, it is proved that the proposed distributed heuristic adaptive neural networks ensure the convergence of all the agents to the optimum with a single communication among connected neighbors after every training step, which is also suitable for switching graphs. This theorem is verified by the simulation, which gives the results that fewer iterations are required for all agents to reach the optimum using the proposed heuristic adaptive consensus algorithm, and the SVRG can greatly decrease the fluctuations caused by the stochastic gradient and improve its performance with only a little extra computational cost.

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“…Multisystem network is a complex dynamical network, which has received great attraction due to its many applications in various elds, such as secure communications and biological systems. e dynamical characteristics of multisystem networks have been paid more and more attention in the elds of science and engineering (see [1][2][3][4]). As we know, synchronization of a coupling system means that its all subsystems produce common behavior under di erent initial values.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, we notice that there are some non-Laplacian coupling in complex dynamical networks, so we can try to achieve the synchronization of multisystem networks by constructing non-Laplacian coupling matrix. In addition, in recent decades, NNs have become a hot research topic because of its rich content and wide application; therefore, there are many results in the research of NNs, such as in [1,[5][6][7][8][9][10][11][12]. e authors in [1] proposed a new ETS to achieve the synchronization of multiple NNs (MNNs).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in recent decades, NNs have become a hot research topic because of its rich content and wide application; therefore, there are many results in the research of NNs, such as in [1,[5][6][7][8][9][10][11][12]. e authors in [1] proposed a new ETS to achieve the synchronization of multiple NNs (MNNs). e dynamical characteristics of nonautonomous fractional-order delay NNs were studied in [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event‐Triggered Strategy

Zhang

Liu

2019

Complexity

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This paper proposes the event-triggered strategy (ETS) for multiple neural networks (NNs) with parameter uncertainty and time delay. By establishing event-triggered mechanism and using matrix inequality techniques, several sufficient criteria are obtained to ensure global robust exponential synchronization of coupling NNs. In particular, the coupling matrix need not be the Laplace matrix in this paper. In addition, the lower bounds of sampling time intervals are also found by the established event-triggered mechanism. Eventually, three numerical examples are offered to illustrate the obtained results.

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Event-Triggered Synchronization Strategy for Multiple Neural Networks With Time Delay

Cited by 44 publications

References 51 publications

Distributed multiple-bipartite consensus in networked Lagrangian systems with cooperative-competitive interactions

Distributed multiple-bipartite consensus in networked Lagrangian systems with cooperative-competitive interactions

Distributed Heuristic Adaptive Neural Networks With Variance Reduction in Switching Graphs

Global Robust Exponential Synchronization of Multiple Uncertain Neural Networks Subject to Event‐Triggered Strategy

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