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The performance evaluation of diagonal recurrent neural network with different chaos neurons

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Abstract

In this paper, different chaos neurons are added in hidden layer of diagonal recurrent neural network. The advanced networks can solve the problem of long training time because of the convergence of chaos neuron. The Logistic map, the Chebyshev map, and the Sine map are used to construct networks. These networks are applied for image compression in order to compare their performance. The result of simulation test shows that the networks with chaos neurons are superior to traditional diagonal recurrent network in the effect of image reconstruction, and the networks with different chaotic maps are analyzed and compared for the first time.

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Acknowledgments

This research is supported by the science foundation of educational department of Hebei Province (Nos: QN2015125), the research and development program of science and technology of Shijiazhuang (Nos: 151790531A) and the National High Technology Research and Development Program of China (863 Program) (Nos: 2013AA014203).

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Authors and Affiliations

  1. Information Engineering Department, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050061, China

    Yi Zhang, Mingsheng Liu, Boyuan Ma & Yi Zhen

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  1. Yi Zhang
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  2. Mingsheng Liu
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  3. Boyuan Ma
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  4. Yi Zhen
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Correspondence to Yi Zhang.

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Zhang, Y., Liu, M., Ma, B. et al. The performance evaluation of diagonal recurrent neural network with different chaos neurons. Neural Comput & Applic 28, 1611–1618 (2017). https://doi.org/10.1007/s00521-015-2129-z

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  • DOI: https://doi.org/10.1007/s00521-015-2129-z

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