Effect of fuzziness on the stability of inertial neural networks with mixed delay via non-reduced-order method. Issue 3 (2nd October 2019)

• Record Type: Journal Article
• Authors: Aouiti, Chaouki; Abed Assali, El
• Is Part Of: International journal of computer mathematics. Volume 4:Issue 3/4(2019)
• Page Start: 151
• Page End: 170
• Publication Date: 2019-10-02
• Subjects: Inertial neural networks -- time-varying delay -- distributed delays -- global asymptotic stability; Computer systems -- Periodicals; Computer systems; Periodicals; 004
• DOI: 10.1080/23799927.2019.1685006
• Languages: English
• ISSNs: 2379-9927

Abstract

ABSTRACT: In this paper, without transforming the original inertial neural networks into the first-order differential equation by some variable substitutions, fuzziness, time-varying and distributed delays are introduced into inertial networks and the existence, the uniqueness and the asymptotic stability for the neural networks are investigated. The existence of a unique equilibrium point is proved by using inequality techniques, and the properties of an M -matrix. By finding a new Lyapunov–Krasovskii functional, some sufficient conditions are derived ensuring the asymptotic stability. Finally, three numerical examples with simulation are presented to show the effectiveness of our theoretical results.