Existence and global exponential stability of anti-periodic solutions for generalised inertial competitive neural networks with time-varying delays

Qin, Jiali

doi:10.1080/0952813x.2019.1647564

Cited by 10 publications

(1 citation statement)

References 51 publications

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“…Some scholars have also studied the anti-periodicity of inertia neural networks, such as The author of [14] obtained sufficient conditions for the existence and exponential stability of the anti-periodic solution of inertia neural network with variable delays using the Lyapunov method and uniform convergence. In [15,16], the authors gave sufficient conditions for the existence of anti-periodic solutions and global exponential stability of quaternion numerical inertia neural networks with time-varying delays and a class of generalized inertia competitive neural networks with time-varying delays, respectively. The author of [17] followed up with his research on the existence and exponential stability of anti-periodic solutions of fuzzy BAM neural networks with inertia terms and time-varying delays.…”

Section: Introductionmentioning

confidence: 99%

Anti-periodic Solutions Dynamics for Fractional-order Inertia Cohen-Grossberg Neural Networks

Jiang

2023

Preprint

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The dynamic behavior of anti-periodic solutions for fractional-order inertia Cohen-Grossberg neural networks is investigated in the article. First, the fractional derivative with different orders is transformed to that with the same order by properly variable substitution; Second, a sufficient condition can ensure the solution is global Mittag-Leffler stability by using properties of fractional calculus and characteristics of Mittag-Leffler function; Moreover, a sufficient condition for the existence of an anti-periodic solution is given by constructing a system sequence solution that converges to a continuous function using Arzela-Asolitheorem. In the final, we verify the correctness of the conclusion by numerical simulation.

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Section: Introductionmentioning

confidence: 99%

Anti-periodic Solutions Dynamics for Fractional-order Inertia Cohen-Grossberg Neural Networks

Jiang

2023

Preprint

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Dissipativity Analysis of Memristive Inertial Competitive Neural Networks with Mixed Delays

Yang,

Jian

2024

Neural Process Lett

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Without altering the inertial system into the two first-order differential systems, this paper primarily works over the global exponential dissipativity (GED) of memristive inertial competitive neural networks (MICNNs) with mixed delays. For this purpose, a novel differential inequality is primarily established around the discussed system. Then, by applying the founded inequality and constructing some novel Lyapunov functionals, the GED criteria in the algebraic form and the linear matrix inequality (LMI) form are given, respectively. Furthermore, the estimation of the global exponential attractive set (GEAS) is furnished. Finally, a specific illustrative example is analyzed to check the correctness and feasibility of the obtained findings.

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Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach

Han

et al. 2021

Chaos, Solitons & Fractals

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Existence and global exponential stability of anti-periodic solutions for generalised inertial competitive neural networks with time-varying delays

Cited by 10 publications

References 51 publications

Anti-periodic Solutions Dynamics for Fractional-order Inertia Cohen-Grossberg Neural Networks

Anti-periodic Solutions Dynamics for Fractional-order Inertia Cohen-Grossberg Neural Networks

Dissipativity Analysis of Memristive Inertial Competitive Neural Networks with Mixed Delays

Stabilization of inertial Cohen-Grossberg neural networks with generalized delays: A direct analysis approach

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