Robust stability and passivity analysis for discrete-time neural networks with mixed time-varying delays via a new summation inequality

AIMS Mathematics

article

Jenjira Thipcha¹  Presarin Tangsiridamrong²  Thongchai Botmart²  Boonyachat Meesuptong²  M. Syed Ali³  Pantiwa Srisilp⁴  Kanit Mukdasai² 

[1] Department of Mathematics, Faculty of Science, Maejo University;Department of Mathematics, Faculty of Science, Khon Kaen University;Department of Mathematics, Thiruvalluvar University;Rail System Institute of Rajamangala University of Technology Isan

关键词: stability analysis;    passivity;    discrete-time neural networks;    interval time-varying delay;    Lypunov-Krasovskii theory;   

DOI  :  10.3934/math.2023249

学科分类：地球科学(综合)

来源: AIMS Press

【 摘 要 】

The summation inequality is essential in creating delay-dependent criteria for discrete-time systems with time-varying delays and developing other delay-dependent standards. This paper uses our rebuilt summation inequality to investigate the robust stability analysis issue for discrete-time neural networks that incorporate interval time-varying leakage and discrete and distributed delays. It is a novelty of this study to consider a new inequality, which makes it less conservative than the well-known Jensen inequality, and use it in the context of discrete-time delay systems. Further stability and passivity criteria are obtained in terms of linear matrix inequalities (LMIs) using the Lyapunov-Krasovskii stability theory, coefficient matrix decomposition technique, mobilization of zero equation, mixed model transformation, and reciprocally convex combination. With the assistance of the LMI Control toolbox in Matlab, numerical examples are provided to demonstrate the validity and efficiency of the theoretical findings of this research.

【 授权许可】

CC BY