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Memristive cyclic three-neuron-based neural network with chaos and global coexisting attractors

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Abstract

It has been documented that a cyclic three-neuron-based neural network with resistive synaptic weights cannot exhibit chaos. Towards this end, a memristive cyclic three-neuron-based neural network is presented using a memristive weight to substitute a resistive weight. The memristive cyclic neural network always has five equilibrium points within the parameters of interest, and their stability analysis shows that they are one index-2 saddle-focus, two index-1 saddle-foci, and two stable node-foci, respectively. Dynamical analyses are performed for the memristive cyclic neural network by several numerical simulation methods. The results demonstrate that the memristor synapse-based neural network with the simplest cyclic connection can not only exhibit chaos, but also present global coexisting attractors composed of stable points and unstable periodic or chaotic orbits under different initial conditions. Besides, with the designed implementation circuit, Multisim circuit simulations and hardware experiments are executed to validate the numerical simulations.

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Authors and Affiliations

  1. School of Microelectronics and Control Engineering, Changzhou University, Changzhou, 213164, China

    Han Bao, ZhuGuan Chen, JianMing Cai, Quan Xu & BoCheng Bao

Authors
  1. Han Bao
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  2. ZhuGuan Chen
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  3. JianMing Cai
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  4. Quan Xu
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  5. BoCheng Bao
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Corresponding author

Correspondence to BoCheng Bao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62201094, 62271088 and 12172066), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20210850), and the Scientific Research Foundation of Jiangsu Provincial Education Department, China (Grant No. 22KJB510001).

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Bao, H., Chen, Z., Cai, J. et al. Memristive cyclic three-neuron-based neural network with chaos and global coexisting attractors. Sci. China Technol. Sci. 65, 2582–2592 (2022). https://doi.org/10.1007/s11431-022-2144-x

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  • DOI: https://doi.org/10.1007/s11431-022-2144-x

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