Anti-periodic solutions for cellular neural networks with oscillating coefficients in leakage terms

Anti-periodic solutions for cellular neural networks with oscillating coefficients in leakage terms This paper is concerned with the anti-periodic solutions for a class of cellular neural network model with oscillating coefficients in leakage terms. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions to guarantee the existence and exponential stability of anti-periodic solutions for this model, which improve and supplement existing ones. Moreover, an example and its numerical simulations are given to support the theoretical results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

Anti-periodic solutions for cellular neural networks with oscillating coefficients in leakage terms

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0531-1
Publisher site
See Article on Publisher Site

Abstract

This paper is concerned with the anti-periodic solutions for a class of cellular neural network model with oscillating coefficients in leakage terms. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions to guarantee the existence and exponential stability of anti-periodic solutions for this model, which improve and supplement existing ones. Moreover, an example and its numerical simulations are given to support the theoretical results.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: Apr 23, 2016

References

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