Event-triggered bipartite synchronization of coupled multi-order fractional neural networks

Liu, Peng; Li, Yunliu; Sun, Junwei; Wang, Yanfeng; Wang, Yingcong

doi:10.1016/j.knosys.2022.109733

Cited by 15 publications

(1 citation statement)

References 47 publications

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“…At present, great accomplishments on fractional dynamical systems have been acquired. For example, Rihan and Rajivganthi [28] studied the Hopf bifurcation and stability of a fractional-order delay predator-prey model involving Holling-type III function; Wang et al [29] investigated the bifur-cation phenomenon and stability for a fractional-order delayed predatorprey model owning interspecific competition; Xiao et al [30] discussed controller design of finite-time synchronization in fractional memristive neural network models; Liu et al [31] explored the event-triggered bipartite synchronization issue of coupled fractional-order neural networks. For more concrete contents, one can see [32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Hopf Bifurcation Control of a Fractional-Order Delayed Turbidostat Model via a Novel Extended Hybrid Controller

Xu,

Ou,

Pang

et al. 2023

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Building delayed dynamical models to describe the inherent laws of different chemical matters has become a hot theme in recent years. In this current study, we set up a new fractional-order delayed turbidostat model. By using laplace transform, we obtain the characteristic equation of established fractional-order delayed turbidostat model. By selecting the delay as bifurcation parameter and exploring the roots of the corresponding characteristic equation of the involved fractional-order delayed turbidostat model, a novel delay-dependent condition on stability and Hopf bifurcation is acquired. Taking advantage of a novel extended hybrid controller, the stability region and the time of Hopf bifurcation of the established fractional-order delayed turbidostat model are successfully controlled. The role of delay in stabilizing system and controlling Hopf bifurcation is revealed. Matlab experiments are carried out to check the rationality of the acquired key outcomes in this article. The acquired outcomes of this study are completely new and own great theoretical value in dominating concentrations of various chemical matters.

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Section: Introductionmentioning

confidence: 99%

Hopf Bifurcation Control of a Fractional-Order Delayed Turbidostat Model via a Novel Extended Hybrid Controller

Xu,

Ou,

Pang

et al. 2023

match

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Prescribed-time cluster synchronization of coupled inertial neural networks: a lifting dimension approach

Liu,

Yong,

Sun

et al. 2024

Neural Comput & Applic

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Bipartite Synchronization of Fractional Order Multiple Memristor Coupled Delayed Neural Networks with Event Triggered Pinning Control

Dhivakaran,

Gowrisankar,

Vinodkumar

2024

Neural Process Lett

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This paper investigates the leader and leaderless bipartite synchronization with the signed network utilizing the model of multiple memristor and coupled delayed neural network in an event-triggered pinning control. The usage of the descriptor method in fractional-order neural networks in case of a non-differentiable delay can be seen in this paper. Further, Lyapunov functional criteria, including Lur’e Postnikov Lyapunov functional, is established, and bipartite leader and leaderless synchronization are proved. The obtained numerical results can be seen as accurate to the theoretical results.

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Event-triggered bipartite synchronization of coupled multi-order fractional neural networks

Cited by 15 publications

References 47 publications

Hopf Bifurcation Control of a Fractional-Order Delayed Turbidostat Model via a Novel Extended Hybrid Controller

Hopf Bifurcation Control of a Fractional-Order Delayed Turbidostat Model via a Novel Extended Hybrid Controller

Prescribed-time cluster synchronization of coupled inertial neural networks: a lifting dimension approach

Bipartite Synchronization of Fractional Order Multiple Memristor Coupled Delayed Neural Networks with Event Triggered Pinning Control

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