Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication

Wei, Fei; Chen, Guici; Zeng, Zhigang; Gunasekaran, Nallappan

doi:10.1016/j.neunet.2023.08.015

Cited by 5 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to asymptotic synchronization, the fixed-time synchronization of INNs is also a hot issue at present [35,47,50]. However, there seems to be only a few reports on the fixed-time synchronization of QV-INNs with impulses based on the direct analysis approach.…”

Section: Discussionmentioning

confidence: 99%

Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique

Yu,

Xiong,

2024

Mathematics

View full text Add to dashboard Cite

The asymptotic synchronization of quaternion-valued delayed neural networks with impulses and inertia is studied in this article. Firstly, a convergence result on piecewise differentiable functions is developed, which is a generalization of the Barbalat lemma and provides a powerful tool for the convergence analysis of discontinuous systems. To achieve synchronization, a constant gain-based control scheme and an adaptive gain-based control strategy are directly proposed for response quaternion-valued models. In the convergence analysis, a direct analysis method is developed to discuss the synchronization without using the separation technique or reduced-order transformation. In particular, some Lyapunov functionals, composed of the state variables and their derivatives, are directly constructed and some synchronization criteria represented by matrix inequalities are obtained based on quaternion theory. Some numerical results are shown to further confirm the theoretical analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique

Yu,

Xiong,

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…In recent years, there have been significant advancements in the study of finite-time synchronization (FTS) for memristive neural network systems. For instance, see [36][37][38][39][40][41][42][43]. Li et al [36] investigated FTS for a class of drive-response FMNNs with discontinuous activation functions.…”

Section: Introductionmentioning

confidence: 99%

“…Guo et al [40] proposed FTS of drive-response inertial MNNs with time delay. Wei et al [41], utilizing interval-matrix-based methods, investigated the FTS/FTDS of delayed inertial MNNs. Gong et al [42] focused on the FTS problem of fuzzy MNNs with time delay.…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Adaptive Synchronization and Fixed-Time Synchronization of Fractional-Order Memristive Cellular Neural Networks with Time-Varying Delays

Liu,

Sun

2024

Mathematics

View full text Add to dashboard Cite

Asymptotic synchronization requires continuous external control of the system, which is unrealistic considering the cost of control. Adaptive control methods have strong robustness to uncertainties such as disturbances and unknowns. On the other hand, for finite-time synchronization, if the initial value of the system is unknown, the synchronization time of the finite-time synchronization cannot be estimated. This paper explores the finite-time adaptive synchronization (FTAS) and fixed-time synchronization (FDTS) of fractional-order memristive cellular neural networks (FMCNNs) with time-varying delays (TVD). Utilizing the properties and principles of fractional order, we introduce a novel lemma. Based on this lemma and various analysis techniques, we establish new criteria to guarantee FTAS and FDTS of FMCNNs with TVD through the implementation of a delay-dependent feedback controller and fractional-order adaptive controller. Additionally, we estimate the upper bound of the synchronization setting time. Finally, numerical simulations are conducted to confirm the validity of the finite-time and fixed-time stability theorems.

show abstract

Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method

Wang,

Shi,

Wang

et al. 2024

Expert Systems with Applications

View full text Add to dashboard Cite

Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication

Cited by 5 publications

References 39 publications

Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique

Synchronization Analysis for Quaternion-Valued Delayed Neural Networks with Impulse and Inertia via a Direct Technique

Finite-Time Adaptive Synchronization and Fixed-Time Synchronization of Fractional-Order Memristive Cellular Neural Networks with Time-Varying Delays

Synchronization sampled-data control of uncertain neural networks under an asymmetric Lyapunov–Krasovskii functional method

Contact Info

Product

Resources

About