Open AccessRobust passivity analysis of mixed delayed neural networks with interval nondifferentiable time-varying delay based on multiple integral approach
Author(s) -
Thongchai Botmart,
Sorphorn Noun,
Kanit Mukdasai,
Wajaree Weera,
Narongsak Yotha
Publication year - 2021
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2021170
Subject(s) - passivity , artificial neural network , interval (graph theory) , bounded function , matlab , control theory (sociology) , linear matrix inequality , multiple integral , mathematics , norm (philosophy) , computer science , activation function , toolbox , mathematical optimization , artificial intelligence , control (management) , mathematical analysis , combinatorics , law , political science , electrical engineering , programming language , engineering , operating system
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.