title: Stability analysis of Cohen-Grossberg neural networks.
abstract: Without assuming boundedness and differentiability of the activation functions and any symmetry of interconnections, we employ Lyapunov functions to establish some sufficient conditions ensuring existence, uniqueness, global asymptotic stability, and even global exponential stability of equilibria for the Cohen-Grossberg neural networks with and without delays. Our results are not only presented in terms of system parameters and can be easily verified and also less restrictive than previously known criteria and can be applied to neural networks, including Hopfield neural networks, bidirectional association memory neural networks, and cellular neural networks.
authors.affiliation: College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, China. shangjguo@hnu.cn
authors.forename: Shangjiang
authors.initials: S
authors.lastname: Guo
authors.forename: Lihong
authors.initials: L
authors.lastname: Huang