Periodic solution for inertial neural networks with variable parameters

Lingping Zhang; Bo Du; Lingping Zhang; Bo Du

doi:10.3934/math.2021789

AIMS Mathematics

2021, Volume 6, Issue 12: 13580-13591. doi: 10.3934/math.2021789

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Research article

Periodic solution for inertial neural networks with variable parameters

Lingping Zhang ,
Bo Du ^,

School of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, China

Received: 29 August 2021 Accepted: 22 September 2021 Published: 24 September 2021
MSC : 34C25, 34C13

We discuss periodic solution problems and asymptotic stability for inertial neural networks with $ D- $operator and variable parameters. Based on Mawhin's continuation theorem and Lyapunov functional method, some new sufficient conditions on the existence and asymptotic stability of periodic solutions are established. Finally, a numerical example verifies the effectiveness of the obtained results.
- periodic solution,
- asymptotic stability,
- inertial neural networks
Citation: Lingping Zhang, Bo Du. Periodic solution for inertial neural networks with variable parameters[J]. AIMS Mathematics, 2021, 6(12): 13580-13591. doi: 10.3934/math.2021789

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Abstract

We discuss periodic solution problems and asymptotic stability for inertial neural networks with $ D- $operator and variable parameters. Based on Mawhin's continuation theorem and Lyapunov functional method, some new sufficient conditions on the existence and asymptotic stability of periodic solutions are established. Finally, a numerical example verifies the effectiveness of the obtained results.

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