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A GCM neural network using cubic logistic map for information processing

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Abstract

A new chaotic neural network described by a modified globally coupled map (GCM) model with cubic logistic map is proposed, which is called CL-GCM model. Its rich dynamical behaviors over a wide range of parameters and the dynamics mechanism of neurons are demonstrated in detail. Furthermore, the network with delay coupling can be precisely controlled to any specified-periodic orbit by feedback control or modulated parameter control with variable threshold. The results of simulations and experiments suggest that the network is controlled successfully. The controlled CL-GCM model exhibits excellent associative memory performance which appears it can output unique fixed pattern or periodic patterns with specified period which contain the stored pattern closest to the initial pattern.

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Acknowledgments

We are grateful to the anonymous reviews for their valuable suggestions and comments, which help us to improve this paper. This work is supported by Jilin Postdoctoral Science Foundation funded project (Grant No. RB201355), academic backbone program foundation for youth by Harbin Normal University (Grant No. KGB201222), and Project supported by the Science and Technology Pre-Research Foundations of Harbin Normal University, China (Grant No. 12XYG-04).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Northeast Normal University, Changchun, 130024, China

    Tao Wang

  2. School of Mathematical Sciences, Harbin Normal University, Harbin, 150025, China

    Tao Wang & Nuo Jia

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  1. Tao Wang
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  2. Nuo Jia
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Correspondence to Tao Wang.

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Wang, T., Jia, N. A GCM neural network using cubic logistic map for information processing. Neural Comput & Applic 28, 1891–1903 (2017). https://doi.org/10.1007/s00521-016-2407-4

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  • DOI: https://doi.org/10.1007/s00521-016-2407-4

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