Periodic solutions of Cohen-Grossberg-type Bi-directional associative memory neural networks with neutral delays and impulses

Shuting Chen; Ke Wang; Jiang Liu; Xiaojie Lin; Shuting Chen; Ke Wang; Jiang Liu; Xiaojie Lin

doi:10.3934/math.2021154

AIMS Mathematics

2021, Volume 6, Issue 3: 2539-2558. doi: 10.3934/math.2021154

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Research article

Periodic solutions of Cohen-Grossberg-type Bi-directional associative memory neural networks with neutral delays and impulses

School of Mathematics and Statistics, Jiangsu Normal University, Xuzhou, Jiangsu, 221116, China

Received: 11 October 2020 Accepted: 16 December 2020 Published: 23 December 2020
MSC : 34C25, 92B20

This paper considers a class of delayed Cohen-Grossberg-type bi-directonal associative memory neural networks with impulses. By using Mawhin continuation theorem and constructing a new Lyapunov function, some sufficient conditions are presented to guarantee the existence and stability of periodic solutions for the impulsive neural network systems. A simulation example is carried out to illustrate the efficiency of the theoretical results.
- Cohen-Grossberg,
- BAM neural networks,
- impulse,
- periodic solution,
- stability,
- Mawhin coincidence degree,
- Lyapunov function
Citation: Shuting Chen, Ke Wang, Jiang Liu, Xiaojie Lin. Periodic solutions of Cohen-Grossberg-type Bi-directional associative memory neural networks with neutral delays and impulses[J]. AIMS Mathematics, 2021, 6(3): 2539-2558. doi: 10.3934/math.2021154

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Abstract

This paper considers a class of delayed Cohen-Grossberg-type bi-directonal associative memory neural networks with impulses. By using Mawhin continuation theorem and constructing a new Lyapunov function, some sufficient conditions are presented to guarantee the existence and stability of periodic solutions for the impulsive neural network systems. A simulation example is carried out to illustrate the efficiency of the theoretical results.

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