title: Convergence behavior of delayed discrete cellular neural network without periodic coefficients.
abstract: In this paper, we study convergence behaviors of delayed discrete cellular neural networks without periodic coefficients. Some sufficient conditions are derived to ensure all solutions of delayed discrete cellular neural network without periodic coefficients converge to a periodic function, by applying mathematical analysis techniques and the properties of inequalities. Finally, some examples showing the effectiveness of the provided criterion are given.
authors: College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China.
authors: Jinling
authors: J
authors: Wang
authors: College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China. Electronic address: jianghai@xju.edu.cn.
authors: Haijun
authors: H
authors: Jiang
authors: College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China.
authors: Cheng
authors: C
authors: Hu
authors: College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China.
authors: Tianlong
authors: T
authors: Ma
mesh_terms: D000465:Algorithms; D016571:Neural Networks, Computer; D010507:Periodicity
keywords: Convergence; Discrete cellular neural network; Periodic solution; Without periodic coefficient