Finite-time stability of neutral-type neural networks with random time-varying delays. Issue 15 (18th November 2017)

• Record Type: Journal Article
• Authors: Ali, M. Syed; Saravanan, S.; Zhu, Quanxin
• Is Part Of: International journal of systems science. Volume 48:Issue 15(2017)
• Page Start: 3279
• Page End: 3295
• Publication Date: 2017-11-18
• Subjects: Finite-time stability -- linear matrix inequalities -- Lyapunov–Krasovskii functional -- neural networks -- time-varying delays; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2017.1367434
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov–Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.