Novel stability criterion for DNNs via improved asymmetric LKF

Xianhao Zheng; Jun Wang; Kaibo Shi; Yiqian Tang; Jinde Cao; Xianhao Zheng; Jun Wang; Kaibo Shi; Yiqian Tang; Jinde Cao

doi:10.3934/mmc.2024025

Mathematical Modelling and Control

2024, Volume 4, Issue 3: 307-315. doi: 10.3934/mmc.2024025

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Research article

Novel stability criterion for DNNs via improved asymmetric LKF

1.
Electronic Information Engineering Key Laboratory of Electronic Information of State Ethnic Affairs Commission, College of Electrical Engineering, Southwest Minzu University, Chengdu 610041, China
2.
School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China
3.
School of Mathematics, Southeast University, Nanjing 210096, China
4.
Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea

Received: 25 January 2024 Revised: 23 May 2024 Accepted: 15 June 2024 Published: 05 September 2024

This paper briefly proposes an improved asymmetric Lyapunov-Krasovskii functional to analyze the stability issue of delayed neural networks (DNNs). By utilizing linear matrix inequalities (LMIs) incorporating integral inequality and reciprocally convex combination techniques, a new stability criterion is formulated. Compared to existing methods, the newly developed stability criterion demonstrates less conservatism and complexity in analyzing neural networks. To explicate the potency and preeminence of the proposed stability criterion, a renowned numerical instance is showcased, serving as an illustrative embodiment.
- asymmetric Lyapunov-Krasovskii functional,
- delayed neural network,
- integral inequality,
- stability criterion
Citation: Xianhao Zheng, Jun Wang, Kaibo Shi, Yiqian Tang, Jinde Cao. Novel stability criterion for DNNs via improved asymmetric LKF[J]. Mathematical Modelling and Control, 2024, 4(3): 307-315. doi: 10.3934/mmc.2024025

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Abstract

This paper briefly proposes an improved asymmetric Lyapunov-Krasovskii functional to analyze the stability issue of delayed neural networks (DNNs). By utilizing linear matrix inequalities (LMIs) incorporating integral inequality and reciprocally convex combination techniques, a new stability criterion is formulated. Compared to existing methods, the newly developed stability criterion demonstrates less conservatism and complexity in analyzing neural networks. To explicate the potency and preeminence of the proposed stability criterion, a renowned numerical instance is showcased, serving as an illustrative embodiment.

References

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