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Periodic Solution for Neutral-Type Neural Networks in Critical Case

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Abstract

A generalized neutral-type neural networks in critical case is studied. Some existence and asymptotic behavior results of periodic solution to the neutral-type neural networks in critical case are obtained by continuation theorem of coincidence degree theory and some analysis techniques. Finally, an example is given to show the effectiveness of the results in this paper.

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Acknowledgments

This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, under Grant No. 9-130-36-HiCi, NNSF (No. 11571136) of China and Postdoctoral Foundation of China (2014M561716). The authors, therefore, acknowledge with thanks DSR technical and financial support.

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Authors and Affiliations

  1. Department of Mathematics, Huaiyin Normal University, Huaian, 223300, Jiangsu, People’s Republic of China

    Bo Du

  2. Department of Mathematics, Yangzhou University, Yangzhou, 225002, Jiangsu, People’s Republic of China

    Bo Du & Yurong Liu

  3. College of Mathematics and Physics, NUIST, Nanjing, 210044, Jiangsu, People’s Republic of China

    Shiping Lu

Authors
  1. Bo Du
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  2. Shiping Lu
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  3. Yurong Liu
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Correspondence to Bo Du.

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Du, B., Lu, S. & Liu, Y. Periodic Solution for Neutral-Type Neural Networks in Critical Case. Neural Process Lett 44, 765–777 (2016). https://doi.org/10.1007/s11063-015-9493-7

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  • DOI: https://doi.org/10.1007/s11063-015-9493-7

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