Convergence behavior of delayed discrete cellular neural network without periodic coefficients

Jinling Wang; Haijun Jiang; Cheng Hu; Tianlong Ma

doi:10.1016/j.neunet.2014.01.007

Convergence behavior of delayed discrete cellular neural network without periodic coefficients

Neural Netw. 2014 May:53:61-8. doi: 10.1016/j.neunet.2014.01.007. Epub 2014 Jan 31.

Authors

Jinling Wang¹, Haijun Jiang², Cheng Hu¹, Tianlong Ma¹

Affiliations

¹ College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China.
² College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, PR China. Electronic address: jianghai@xju.edu.cn.

PMID: 24531041
DOI: 10.1016/j.neunet.2014.01.007

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.

Keywords: Convergence; Discrete cellular neural network; Periodic solution; Without periodic coefficient.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Neural Networks, Computer*
Periodicity*