Open AccessImproved results on mixed passive and $ H_{\infty} $ performance for uncertain neural networks with mixed interval time-varying delays via feedback control
Author(s) -
Sunisa Luemsai,
Thongchai Botmart,
Wajaree Weera,
Suphachai Charoensin
Publication year - 2020
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.2021161
Subject(s) - interval (graph theory) , control theory (sociology) , differentiable function , exponential stability , mathematics , artificial neural network , convex combination , controller (irrigation) , stability (learning theory) , matlab , multiple integral , regular polygon , control (management) , computer science , convex optimization , pure mathematics , mathematical analysis , combinatorics , physics , nonlinear system , geometry , artificial intelligence , quantum mechanics , machine learning , agronomy , biology , operating system
This paper studies the mixed passive and $ H_{\infty} $ performance for uncertain neural networks with interval discrete and distributed time-varying delays via feedback control. The interval discrete and distributed time-varying delay functions are not assumed to be differentiable. The improved criteria of exponential stability with a mixed passive and $ H_{\infty} $ performance are obtained for the uncertain neural networks by constructing a Lyapunov-Krasovskii functional (LKF) comprising single, double, triple, and quadruple integral terms and using a feedback controller. Furthermore, integral inequalities and convex combination technique are applied to achieve the less conservative results for a special case of neural networks. By using the Matlab LMI toolbox, the derived new exponential stability with a mixed passive and $ H_{\infty} $ performance criteria is performed in terms of linear matrix inequalities (LMIs) that cover $ H_{\infty} $, and passive performance by setting parameters in the general performance index. Numerical examples are shown to demonstrate the benefits and effectiveness of the derived theoretical results. The method given in this paper is less conservative and more general than the others.