Novel stability criterion for DNNs via improved asymmetric LKF

Novel stability criterion for DNNs via improved asymmetric LKF

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 criteri...

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Bibliographic Details
Main Authors: Xianhao Zheng, Jun Wang, Kaibo Shi, Yiqian Tang, Jinde Cao
Format: Article
Language:English
Published: AIMS Press 2024-09-01
Series:Mathematical Modelling and Control
Subjects:
asymmetric lyapunov-krasovskii functional
delayed neural network
integral inequality
stability criterion
Online Access:https://www.aimspress.com/article/doi/10.3934/mmc.2024025
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Summary: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.
ISSN:2767-8946

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