 A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networksZeyu Dong, Xin Wang and Xian Zhang Applied Mathematics and Computation, 2020, vol. 385, issue C Abstract: This paper focuses on the problem of global exponential stability analysis for high-order delayed discrete-time Cohen–Grossberg neural networks. Multiple time-varying delays are considered. First, a technique lemma is obtained based on the properties of nonsingular M-matrices. Second, the delay-dependent and -independent criteria under which the zero equilibrium is globally exponentially stable are derived, respectively. Last, the validity of these criteria are illustrated by a pair of numerical examples. Compared with the previous results, the merits of the proposed method are: (i) no Lyapunov–Krasovskii functional or auxiliary function is required; (ii) less computational complexity is verified; and (iii) the obtained stability criteria can easily be realized, since they are to test whether a matrix is nonsingular M-matrix. Keywords: High-order neural networks; Global exponential stability; Nonsingular M-matrix; Multiple time-varying delays (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:385:y:2020:i:c:s0096300320303635 DOI: 10.1016/j.amc.2020.125401 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
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