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Weighted pseudo-almost periodic delayed cellular neural networks

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Abstract

This paper investigates a class of non-autonomous cellular neural networks with mixed delays. Based on the basic theory of the weighted pseudo-almost periodic functions, several sufficient conditions are established to ensure that every solution of the addressed model exponentially tends to a weighted pseudo-almost periodic solution as \(t\rightarrow +\infty\), which generalize some existing ones. In particular, some numerical examples are also given.

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Acknowledgements

My deepest gratitude goes to the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially. Also, I would like to express the sincere appreciation to Prof. Bingwen Liu (Jiaxing University, Zhejiang, China) for the helpful discussion when this work was being carried out. This work was supported by the Scientific Research Foundation of Hunan Provincial Education Department (Grant No. 13A093), and the “Twelfth five-year” education scientific planning project of Hunan province (XJK014CGD084).

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  1. School of Mathematics Finance Xiangnan, University Chenzhou, Chenzhou, 423000, Hunan, People’s Republic of China

    Yanli Xu

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  1. Yanli Xu
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Correspondence to Yanli Xu.

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Xu, Y. Weighted pseudo-almost periodic delayed cellular neural networks. Neural Comput & Applic 30, 2453–2458 (2018). https://doi.org/10.1007/s00521-016-2820-8

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