Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networks: An event-triggered approach

Jia, Hebao; Luo, Dongmei; Wang, Jing; Shen, Hao

doi:10.1016/j.knosys.2022.109104

Cited by 21 publications

(10 citation statements)

References 39 publications

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“…Remark 1 Compared with the existing results [43,[51][52][53], the following are the key elements and advantages of this paper:…”

Section: Practically Exponential Input-to-state Stabilization Of Srdd...mentioning

confidence: 87%

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Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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The stabilization problem for a class of stochastic reaction-diffusion delayed Cohen-Grossberg neural networks (SRDDCGNNs) with event-triggered controller is addressed in this paper. To address such a problem, Neumann boundary condition, distributed and boundary external disturbances are introduced. New sufficient criteria are derived by using the Lyapunov method, event-triggered mechanism, and the linear matrix inequality (LMI) approach to ensure the proposed controlled systems achieve practically exponential input-to-state stabilization. In light of these criteria, the impact of an event-triggered controller on practically exponential input-to-state stability (PEISS) is examined. Furthermore , the obtained results are successfully applied to stochastic reaction-diffusion delayed cellular neural networks (SRDDCNNs) and stochastic reaction-diffusion delayed Hopfield neural networks (SRD-DHNNs). At last, simulation results are given to illustrate the main results, and the SRDDHNNs are applied to the image encryption.

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“…Remark 1 Compared with the existing results [43,[51][52][53], the following are the key elements and advantages of this paper:…”

Section: Practically Exponential Input-to-state Stabilization Of Srdd...mentioning

confidence: 87%

“…In recent decades, the event-triggered control mechanism has been introduced to NNs [48][49][50]. Especially, in [51], the author studied the fixedtime synchronization of inertial CGNNs via event-triggered control. In [52], the author studied the asymptotic synchronization of memristive CGNNs via event-triggered control.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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“…In order to accomplish the goal of accomplishing the intended synchronization in network model (1), it is imperative to configure a control protocol, that is, the control must be designed in such a way that matched disturbances are rejected, actuator faults are tolerated and the synchronization error system (5) must be asymptotically stable. Therefore, an unified control framework is built in the following form by merging the disturbance estimation and fault-tolerant control scheme: where  denotes the controller gain matrix that will be reckoned later; ρm (t) signifies the estimation of the disturbance 𝜌 m (t); Ω represents actuator fault matrix which is of the structure Ω = diag{𝜖 1 , 𝜖 2 , … , 𝜖 k }.…”

Section: Design Of Fault-tolerant Anti-disturbance Controlmentioning

confidence: 99%

“…In recent decades, research on neural networks (NNs) has gained a prominent attention in the academic community because of its extensive applications in a wide range of fields such as deep learning, image processing, signal processing, and so forth. 1 Specifically, neurons and synapses are the two components of neural connections in neural networks that are considered to be of the utmost importance. Therein, synapses play an important part in receiving signals as well as in the plasticity and dynamic information memory of neuronal transmissions.…”

Section: Introductionmentioning

confidence: 99%

Unified synchronization and fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks

Satheesh,

Sakthivel,

Aravinth

et al. 2023

Intl J Robust & Nonlinear

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SummaryThis work primarily concentrates on the design of fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks subject to time delay, matched and mismatched disturbances. Moreover, in the addressed network model, we consider parameter uncertainties and actuator faults. Firstly in order to estimate the matched disturbances generated by the exogenous system, a disturbance observer is devised. Whereas, the mismatched part is tackled by employing the mixed and passivity performance indexes. Subsequently, a unified controller is designed by incorporating error feedback control and the disturbance estimate. Further, with the assistance of Lyapunov stability theory and linear matrix inequality technique, an adequate criteria is procured to ascertain the required synchronization criteria for the assayed network model with the mixed and passivity performance indexes. Following this, by basing on the established conditions, the explicit form of the controller and observer gain matrices is obtained. In the end, a numerical example with simulation results is shown to confirm the potential and usefulness of the conclusions acquired from the theoretical analysis.

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“…In order to cope with this difficult issue, Polyakov [12] introduced FXT stability concept, in which its ST does not depend on the initial values of the system but only relevant on the system parameters and the controller gains. On this basis, a great number of scholars have studied FXT synchronization of various types of nonlinear systems [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Fixed/Predefined-Time Synchronization of Complex-Valued Stochastic BAM Neural Networks with Stabilizing and Destabilizing Impulse

2022

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This article is mainly concerned with the fixed-time and predefined-time synchronization problem for a type of complex-valued BAM neural networks with stochastic perturbations and impulse effect. First, some previous fixed-time stability results on nonlinear impulsive systems in which stabilizing and destabilizing impulses were separately analyzed are extended to a general case in which the stabilizing and destabilizing impulses can be handled simultaneously. Additionally, using the same logic, a new predefined-time stability lemma for stochastic nonlinear systems with a general impulsive effect is obtained by using the inequality technique. Then, based on these novel results, two novel controllers are implemented to derive some simple fixed/predefined-time synchronization criteria for the considered complex-valued impulsive BAM neural networks with stochastic perturbations using the non-separation method. Finally, two numerical examples are given to demonstrate the feasibility of the obtained results.

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Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networks: An event-triggered approach

Cited by 21 publications

References 39 publications

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Unified synchronization and fault‐tolerant anti‐disturbance control for synchronization of multiple memristor‐based neural networks

Fixed/Predefined-Time Synchronization of Complex-Valued Stochastic BAM Neural Networks with Stabilizing and Destabilizing Impulse

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