title: Periodic solutions and exponential stability in delayed cellular neural networks.
abstract: Some simple sufficient conditions are given ensuring global exponential stability and the existence of periodic solutions of delayed cellular neural networks (DCNNs) by constructing suitable Lyapunov functionals and some analysis techniques. These conditions are easy to check in terms of system parameters and have important leading significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. In addition, two examples are given to illustrate the theory.
authors.affiliation: Adult Education College, Yunnan University, Kunming 650091, People's Republic of China. jdcao@ynu.edu.cn
authors.forename: J
authors.initials: J
authors.lastname: Cao
mesh_terms: D000818:Animals; D015233:Models, Statistical; D009415:Nerve Net; D009991:Oscillometry; D015398:Signal Transduction