Synchronization for impulsive hybrid-coupled reaction-diffusion neural networks with time-varying delays

Wu, Kaixiong; Li, Bing; Du, Yuwei; Du, Shishi

doi:10.1016/j.cnsns.2019.105031

Cited by 22 publications

(4 citation statements)

References 38 publications

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“…In fact, the time delays will affect the quality of the network, and even make the system oscillate, diverge and unstable. In [25], the influence of time‐varying delays on neural network synchronization is considered by authors. In [26], the neural network models with multiple time‐varying delays and infinite distributed delays are established.…”

Section: Introductionmentioning

confidence: 99%

Finite‐time synchronization and identification of the Markovian switching delayed network with multiple weights

Guo

Wang²,

Yang

2021

IET Control Theory & Appl

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This paper focuses on finite‐time synchronization and parameters identification in the Markovian switching complex delayed network with multiple weights. Considering the control cost required for network synchronization, finite‐time control technique and pinning control strategy are adopted. Based on these methods, when finite‐time synchronization of the network is achieved, the unknown parameters of the system can also be identified in finite time. In addition, in order to solve the problem of the network performance change caused by network topology switching, an Optimal Nodes Selection Control Strategy (ONSCS) is proposed. On the one hand, finite‐time synchronization and identification of the network can be realized faster, on the other hand, it also further reduces the energy consumption and control cost of the network. Finally, two sets of comparative numerical simulations are given to prove the superiority and applicability of the proposed ONSCS.

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Section: Introductionmentioning

confidence: 99%

Finite‐time synchronization and identification of the Markovian switching delayed network with multiple weights

Guo

Wang²,

Yang

2021

IET Control Theory & Appl

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“…Because of the limited conversion performance of amplifiers and inherent time for information transfer between neurons, time delay is inevitable and may cause some complex dynamical phenomena including oscillatory property, bifurcation, chaos and even instability. To date, many efforts have focused on the dynamic behaviors of time-delayed NNs [7][8][9][10][11][12][13]. Because of the diversity of time delay itself, the discussion of mixed time delay is representative.…”

Section: Introductionmentioning

confidence: 99%

$$H_\infty $$ State Estimation for Round-Robin Protocol-Based Markovian Jumping Neural Networks with Mixed Time Delays

Zou

et al. 2021

Neural Process Lett

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This paper discusses the H ∞ state estimation issue in regard to Markovian jumping neural networks (MJNNs) under the scheduling of the Round-Robin protocol (RRP). The model takes into account mixed time-delays, sensor nonlinearities and exogenous disturbances, making it relatively general and comprehensive. The transmission of MJNNs signals invoked a communication scheme in which the RRP is used for the data transmissions in order to avoid undesirable data collisions. Protocol-dependent state estimator modeling of a hybrid switching system with mixed time delays and disturbances is designed for the first time to achieve asymptotic tracing for the neuron state. Using the Lyapunov stability theory and several asymptotic methods, sufficient conditions for guaranteeing the asymptotic stability of the state estimation are established under the constraint of H ∞ performance. By employing a combination of matrix analysis techniques, the estimator gain matrices are calculated by the feasible solutions to the linear matrix inequalities (LMIs). Finally, a numerical example and related simulations demonstrate the validity of the proposed model. KeywordsMarkovian jumping neural networks • H ∞ state estimation • Round-Robin protocol • Mixed time delays B Bing Li

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“…In human social activities, complex networks are closely related to our lives. Among them, real physical networks (e.g., power networks, transportation networks, and Internet networks) and abstract networks (e.g., financial networks, media networks, and interpersonal networks) both belong to complex networks [1,2]. In the many dynamic behaviors of complex networks, synchronization is one of the representative and important collective behaviors on complex networks [3,4].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Antisynchronization and Parameters Identification of Nonlinear Coupled Multiple-Weight Markovian Switching Complex Networks with Stochastic Perturbations

Qiu¹,

Weng²,

Yu³

et al. 2020

Advances in Mathematical Physics

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The paper mainly focuses on the synchronization of multiple-weight Markovian switching complex networks under nonlinear coupling mode. Based on the finite-time stability theory, Itô’s lemma, and some inequality technologies, the synchronization criterion of network models in the nonlinear coupling mode is obtained; at the same time, unknown parameters of networks are also identified by an effective controller. In addition, several corollaries are given to illustrate the general applicability of the control rules in the paper. Finally, two typical numerical simulations are given to prove the rationality and feasibility of theoretical analysis of network models.

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Synchronization for impulsive hybrid-coupled reaction-diffusion neural networks with time-varying delays

Cited by 22 publications

References 38 publications

Finite‐time synchronization and identification of the Markovian switching delayed network with multiple weights

Finite‐time synchronization and identification of the Markovian switching delayed network with multiple weights

$$H_\infty $$ State Estimation for Round-Robin Protocol-Based Markovian Jumping Neural Networks with Mixed Time Delays

Finite-Time Antisynchronization and Parameters Identification of Nonlinear Coupled Multiple-Weight Markovian Switching Complex Networks with Stochastic Perturbations

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