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Multiperiodicity and Exponential Attractivity of Neural Networks with Mixed Delays

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Abstract

This paper studies the multiperiodicity and exponential attractivity evoked by periodic external inputs in cellular neural networks with discrete delays and distributed delays. We show that an n-neuron cellular neural network with mixed delays can have \(2^n\) periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. Besides, some conditions for ascertaining periodic orbits to be locally exponentially attractive and allowing them to locate in any designated region are derived. The simulation results show that the proposed results are effective.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61472195, 61304093), the Taishan Scholar Project Fund of Shandong Province of China, the Natural Science Foundation of Shandong (ZR2012DL11), and the Applied Basic Research Project of Qingdao (14-2-4-116-jch)

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  1. College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266042, People’s Republic of China

    Yan Ji & Feng Ding

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  1. Yan Ji
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  2. Feng Ding
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Correspondence to Yan Ji.

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Ji, Y., Ding, F. Multiperiodicity and Exponential Attractivity of Neural Networks with Mixed Delays. Circuits Syst Signal Process 36, 2558–2573 (2017). https://doi.org/10.1007/s00034-016-0420-6

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  • DOI: https://doi.org/10.1007/s00034-016-0420-6

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