Novel extended dissipativity criteria for generalized neural networks with interval discrete and distributed time-varying delays

Advances in Difference Equations

article

Luemsai, Sunisa¹  Botmart, Thongchai¹  Weera, Wajaree² 

[1] Department of Mathematics, Khon Kaen University;Department of Mathematics, University of Pha Yao

关键词: Generalized neural networks;    Extended dissipativity analysis;    Asymptotic stability;    Interval discrete and distributed time-varying delays;   

DOI  :  10.1186/s13662-020-03210-x

学科分类：航空航天科学

来源: SpringerOpen

【 摘 要 】

The problem of asymptotic stability and extended dissipativity analysis for the generalized neural networks with interval discrete and distributed time-varying delays is investigated. Based on a suitable Lyapunov–Krasovskii functional (LKF), an improved Wirtinger single integral inequality, a novel triple integral inequality, and convex combination technique, the new asymptotic stability and extended dissipativity criteria are achieved for the generalized neural networks with interval discrete and distributed time-varying delays. By the above methods, the less conservative asymptotic stability criteria are obtained for a special case of the generalized neural networks. By using the Matlab LMI toolbox, the derived new asymptotic stability and extended dissipativity criteria are expressed in terms of linear matrix inequalities (LMIs) that cover$H_{\infty }$ ,$L_{2}$ – $L_{\infty }$ , passivity, and dissipativity performance by setting parameters in the general performance index. Finally, we show numerical examples which are less conservative than other examples in the literature. Moreover, we present numerical examples for asymptotic stability and extended dissipativity performance of the generalized neural networks, including a special case of the generalized neural networks.

【 授权许可】

CC BY