title: Passivity of memristor-based BAM neural networks with different memductance and uncertain delays.
abstract: This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.
authors.affiliation: Department of Mathematics, National Institute of Technology, Tiruchirappalli, 620 015 India.
authors.forename: R
authors.initials: R
authors.lastname: Anbuvithya
authors.affiliation: Department of Mathematics, Anna University-Regional Centre, Coimbatore, 641 047 India.
authors.forename: K
authors.initials: K
authors.lastname: Mathiyalagan
authors.affiliation: Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore, 641 010 Tamil Nadu India ; Department of Mathematics, Sungkyunkwan University, Suwon, 440-746 The Republic of Korea.
authors.forename: R
authors.initials: R
authors.lastname: Sakthivel
authors.affiliation: Department of Mathematics, Periyar University, Salem, 636 011 India.
authors.forename: P
authors.initials: P
authors.lastname: Prakash
keywords: BAM neural networks; Linear matrix inequality; Memristor; Passivity; Uncertain delay