Robust passivity analysis of mixed delayed neural networks with interval nondifferentiable time-varying delay based on multiple integral approach

AIMS Mathematics

Sorphorn Noun¹  Kanit Mukdasai¹  Thongchai Botmart¹  Wajaree Weera²  Narongsak Yotha³ 

[1] 1. Department of Mathematics, Khon Kaen University, Khon Kaen 40002, Thailand;2. Department of Mathematics, University of Pha Yao, Pha Yao 56000, Thailand;3. Department of Applied Mathematics and Statistics, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand;

关键词: passivity analysis;    neural networks;    uncertainties;    nondifferentiable delay;    time-varying delays;   

DOI  :  10.3934/math.2021170

来源: DOAJ

【 摘 要 】

New results on robust passivity analysis of neural networks with interval nondifferentiable and distributed time-varying delays are investigated. It is assumed that the parameter uncertainties are norm-bounded. By construction an appropriate Lyapunov-Krasovskii containing single, double, triple and quadruple integrals, which fully utilize information of the neuron activation function and use refined Jensen's inequality for checking the passivity of the addressed neural networks are established in linear matrix inequalities (LMIs). This result is less conservative than the existing results in literature. It can be checked numerically using the effective LMI toolbox in MATLAB. Three numerical examples are provided to demonstrate the effectiveness and the merits of the proposed methods.

【 授权许可】

Unknown