title: Improved delay-dependent stability condition of discrete recurrent neural networks with time-varying delays.
abstract: This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.
authors.affiliation: Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, Zhejiang, China. nashwzhg@yahoo.com.cn
authors.forename: Zhengguang
authors.initials: Z
authors.lastname: Wu
authors.forename: Hongye
authors.initials: H
authors.lastname: Su
authors.forename: Jian
authors.initials: J
authors.lastname: Chu
authors.forename: Wuneng
authors.initials: W
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors