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Equilibria and Stability Analysis of Cohen-Grossberg BAM Neural Networks on Time Scale

LIU Mingshuo, FANG Yong, DONG Huanhe   

  1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China
  • Received:2020-10-09 Revised:2021-06-09 Online:2022-08-25 Published:2022-08-02
  • Contact: DONG Huanhe,Email:donghuanhe@126.com
  • Supported by:
    This research was supported by the National Natural Science Foundation of China under Grant Nos. 12105161, 11975143 and the Natural Science Foundation of Shandong Province under Grant No. ZR2019QD018.

LIU Mingshuo, FANG Yong, DONG Huanhe. Equilibria and Stability Analysis of Cohen-Grossberg BAM Neural Networks on Time Scale[J]. Journal of Systems Science and Complexity, 2022, 35(4): 1348-1373.

This paper considers the Cohen-Grossberg BAM neural networks (CG-BAMNNs) on time scale, which can unify and generalize the continuous and discrete systems. First, the criteria for the existence and uniqueness of the equilibrium of CG-BAMNNs are derived on time scale. Then based on that, the authors give the criteria for the stability and estimation of equilibrium of the CG-BAMNNs on time scale. The method proposed in this paper unifies and generalizes the continuous and discrete CGBAMNNs systems, and is applicable to some other neural network systems on time scale with practical meaning. The effectiveness of the proposed criteria for delayed CG-BAMNNs is demonstrated by numerical simulation.
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