title: Global robust asymptotic stability of variable-time impulsive BAM neural networks.
abstract: In this paper, the global robust asymptotic stability of the equilibrium point for a more general class of bidirectional associative memory (BAM) neural networks with variable time of impulses is addressed. Unlike most existing studies, the case of non-fix time impulses is focused on in the present study. By means of B-equivalence method, which was introduced in Akhmet (2003, 2005, 2009, 2010), Akhmet and Perestyuk (1990) and Akhmet and Turan (2009), we reduce these networks to a fix time impulsive neural networks system. Sufficient conditions ensuring the existence, uniqueness and global robust asymptotic stability of the equilibrium point are obtained by employing an appropriate Lyapunov function and linear matrix inequality (LMI). Finally, we give one illustrative example to show the effectiveness of the theoretical results.
authors.affiliation: Department of Mathematics, Middle East Technical University, 06531 Ankara, Turkey. Electronic address: msayli@metu.edu.tr.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Saylı
authors.affiliation: Gazi University, Polatlı Faculty of Science and Arts, Department of Mathematics, 06900 Ankara, Turkey; Institute of Applied Mathematics, Middle East Technical University, 06531 Ankara, Turkey. Electronic address: enesyilmaztr@gmail.com.
authors.forename: Enes
authors.initials: E
authors.lastname: Yılmaz
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D013997:Time Factors
keywords: Asymptotic stability; Global robust asymptotic stability; Impulsive BAM neural networks; Linear matrix inequality