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Coexistence and Local Exponential Stability of Multiple Equilibria in Memristive Neural Networks with a Class of General Nonmonotonic Activation Functions

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Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

This paper addresses the multistability problem of n-dimensional memristive neural networks with a class of general nonmonotonic activation functions. Sufficient conditions are proposed for checking the existence of \((2l+3)^n\) equilibria, of which \((l+2)^n\) equilibria are locally exponentially stable. The obtained stability results improve and extend the existing ones. One numerical example is given to illustrate the effectiveness of the obtained results.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61503338 and 61502422) and the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LQ15F030005, LQ15F020006 and LQ15F020008).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Yujiao Huang, Shijun Chen, Jie Xiao & Pengyi Hao

Authors
  1. Yujiao Huang
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  2. Shijun Chen
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  3. Jie Xiao
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  4. Pengyi Hao
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Corresponding author

Correspondence to Yujiao Huang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Huang, Y., Chen, S., Xiao, J., Hao, P. (2017). Coexistence and Local Exponential Stability of Multiple Equilibria in Memristive Neural Networks with a Class of General Nonmonotonic Activation Functions. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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