title: Global asymptotic stability of recurrent neural networks with multiple time-varying delays.
abstract: In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed in our investigation. The results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applied to recurrent neural networks with constant time delays.
authors.affiliation: School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, PR China. hgzhang@ieee.org
authors.forename: Huaguang
authors.initials: H
authors.lastname: Zhang
authors.forename: Zhanshan
authors.initials: Z
authors.lastname: Wang
authors.forename: Derong
authors.initials: D
authors.lastname: Liu
mesh_terms: D000143:Acids / Q000737:chemistry; D000465:Algorithms; D016014:Linear Models; D016571:Neural Networks, Computer
chemical_list: D000143:Acids