title: Dynamic analysis of periodic solution for high-order discrete-time Cohen-Grossberg neural networks with time delays.
abstract: In this paper, we analyze the dynamic behavior of periodic solution for the high-order discrete-time Cohen-Grossberg neural networks (CGNNs) with time delays. First, the existence is studied based on the continuation theorem of coincidence degree theory and Young's inequality. And then, the criterion for the global exponential stability is given using Lyapunov method. Finally, simulation result shows the effectiveness of our proposed criterion.
authors.affiliation: School of Science, Linyi University, Linyi, Shandong 276005, China.
authors.forename: Kaiyun
authors.initials: K
authors.lastname: Sun
authors.forename: Ancai
authors.initials: A
authors.lastname: Zhang
authors.forename: Jianlong
authors.initials: J
authors.lastname: Qiu
authors.forename: Xiangyong
authors.initials: X
authors.lastname: Chen
authors.forename: Chengdong
authors.initials: C
authors.lastname: Yang
authors.forename: Xiao
authors.initials: X
authors.lastname: Chen
mesh_terms: D016571:Neural Networks, Computer; D013997:Time Factors