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Multistability and Multiperiodicity Analysis of Complex-Valued Neural Networks

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Advances in Neural Networks – ISNN 2014 (ISNN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8866))

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Abstract

Multistability and multiperiodicity of neual networks are usually considered in the application of associative memory. In this paper, we study the multistability and multiperiodicity of complex- valued neural networks (CVNNs for short) with one step piecewise linear activation functions. By separating the CVNN into its real and imaginary parts and using state decomposition, we can easily increase the storage capacity by using less neurons. Simulation results are given to illustrative the effectiveness of the theoretical results.

The work described in this paper was supported by the National Natural Science Foundation of China under Grants 61273021 and 61403051 and grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project nos. CUHK417209E and CUHK416811E).

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Author information

Authors and Affiliations

  1. School of Science, Chongqing Jiaotong University, Chongqing, 400074, China

    Jin Hu

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Jun Wang

Authors
  1. Jin Hu
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  2. Jun Wang
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Corresponding author

Correspondence to Jun Wang .

Editor information

Editors and Affiliations

  1. Wuhan, China

    Zhigang Zeng

  2. University of Macau, Macau, Macao

    Yangmin Li

  3. The Chinese University of Hong Kong, Hong Kong, Hong Kong, Hong Kong SAR

    Irwin King

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© 2014 Springer International Publishing Switzerland

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Hu, J., Wang, J. (2014). Multistability and Multiperiodicity Analysis of Complex-Valued Neural Networks. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-12436-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12435-3

  • Online ISBN: 978-3-319-12436-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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