title: LMI-based asymptotic stability analysis of neural networks with time-varying delays.
abstract: The problem of the global asymptotic stability for a class of neural networks with time-varying delays is investigated in this paper, where the activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. By constructing suitable Lyapunov functionals and combining with linear matrix inequality (LMI) technique, new global asymptotic stability criteria about different types of time-varying delays are obtained. It is shown that the criteria can provide less conservative result than some existing ones. Numerical examples are given to demonstrate the applicability of the proposed approach.
authors.affiliation: Department of Information and Communication, Nanjing University of Information, Science and Technology, Nanjing, Jiangsu 210044, China. litaojia79@yahoo.com.cn
authors.forename: Tao
authors.initials: T
authors.lastname: Li
authors.forename: Changyin
authors.initials: C
authors.lastname: Sun
authors.forename: Xianlin
authors.initials: X
authors.lastname: Zhao
authors.forename: Chong
authors.initials: C
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors