Stability Analysis of Discrete-Time Neural Networks With Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality

Zhang, Chuan-Ke, He, Yong, Jiang, Lin ORCID: 0000-0001-6531-2791, Wang, Qing-Guo and Wu, Min (2017) Stability Analysis of Discrete-Time Neural Networks With Time-Varying Delay via an Extended Reciprocally Convex Matrix Inequality. IEEE TRANSACTIONS ON CYBERNETICS, 47 (10). pp. 3040-3049.

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Abstract

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying delay. Assessment of the effect of time delays on system stability requires suitable delay-dependent stability criteria. This paper aims to develop new stability criteria for reduction of conservatism without much increase of computational burden. An extended reciprocally convex matrix inequality is developed to replace the popular reciprocally convex combination lemma (RCCL). It has potential to reduce the conservatism of the RCCL-based criteria without introducing any extra decision variable due to its advantage of reduced estimation gap using the same decision variables. Moreover, a delay-product-type term is introduced for the first time into the Lyapunov function candidate such that a delay-variation-dependent stability criterion with the bounds of delay change rate is established. Finally, the advantages of the proposed criteria are demonstrated through two numerical examples.

Item Type:	Article
Uncontrolled Keywords:	Delay-product-type Lyapunov function, discrete-time neural networks, extended reciprocally convex matrix inequality, interval time-varying delay, stability
Depositing User:	Symplectic Admin
Date Deposited:	16 Oct 2017 11:36
Last Modified:	19 Jan 2023 06:53
DOI:	10.1109/TCYB.2017.2665683
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3010221