Delay-dependent criterion for asymptotic stability of a class of fractional-order memristive neural networks with time-varying delays

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Liping Chen (Author) Other; Tingwen Huang (Author) Other; J. A. Tenreiro Machado (Author) Other; António M. Lopes (Author) FEUP; Yi Chai (Author) Other; Ranchao Wu (Author) Other

Journal

Title: Neural Networks

Vol. 118

Pages: 289-299

ISSN: 0893-6080

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 30 Citations

INSPEC

Other information

Authenticus ID: P-00Q-WBW

DOI: 10.1016/j.neunet.2019.07.006

Abstract (EN): The Lyapunov-Krasovskii functional approach is an important and effective delay-dependent stability analysis method for integer order system. However, it cannot be applied directly to fractional-order (FO) systems. To obtain delay-dependent stability and stabilization conditions of FO delayed systems remains a challenging task. This paper addresses the delay-dependent stability and the stabilization of a class of FO memristive neural networks with time-varying delay. By employing the FO Razumikhin theorem and linear matrix inequalities (LMI), a delay-dependent asymptotic stability condition in the form of LMI is established and used to design a stabilizing state-feedback controller. The results address both the effects of the delay and the FO. In addition, the upper bound of the absolute value of the memristive synaptic weights used in previous studies are released, leading to less conservative conditions. Three numerical simulations illustrate the theoretical results and show their effectiveness.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11