 Stability in impulsive Cohen–Grossberg-type BAM neural networks with time-varying delays: A general analysisKelin Li and Huanglin Zeng Mathematics and Computers in Simulation (MATCOM), 2010, vol. 80, issue 12, 2329-2349 Abstract: In this paper, we investigate a class of impulsive Cohen–Grossberg-type BAM neural networks with time-varying delays. By establishing the delay differential inequality with impulsive initial conditions, and employing the homeomorphism theory, the M-matrix theory and the inequality a∏k=1lbkqk≤(1/r)(ar+∑k=1lqkbkr) (a≥0,bk≥0,qk≥0 with ∑k=1lqk=r−1, and r≥1), some new sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive Cohen–Grossberg-type BAM neural networks with time-varying delays are derived. In particular, the estimate of the exponential convergence rate which depends on the system parameters and the impulsive disturbance intension is also provided. An example is given to show the effectiveness of the results obtained here. Keywords: Bi-directional associative memory; Cohen–Grossberg neural networks; Time-varying delays; Impulses; Global exponential stability (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:matcom:v:80:y:2010:i:12:p:2329-2349 DOI: 10.1016/j.matcom.2010.05.012 Access Statistics for this article Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier |
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