Research article

Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays

  • Received: 17 April 2020 Accepted: 12 June 2020 Published: 23 June 2020
  • MSC : 34C25, 34K13, 34K25

  • Taking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are exponentially attractive to each other. Secondly, by using analytical techniques in uniform convergence functions sequence, the existence of the anti-periodic solution and its global exponential stability are established. Finally, two examples are arranged to illustrate the effectiveness and feasibility of the obtained results.

    Citation: Qian Cao, Xiaojin Guo. Anti-periodic dynamics on high-order inertial Hopfield neural networks involving time-varying delays[J]. AIMS Mathematics, 2020, 5(6): 5402-5421. doi: 10.3934/math.2020347

    Related Papers:

  • Taking into accounting time-varying delays and anti-periodic environments, this paper deals with the global convergence dynamics on a class of anti-periodic high-order inertial Hopfield neural networks. First of all, with the help of Lyapunov function method, we prove that the global solutions are exponentially attractive to each other. Secondly, by using analytical techniques in uniform convergence functions sequence, the existence of the anti-periodic solution and its global exponential stability are established. Finally, two examples are arranged to illustrate the effectiveness and feasibility of the obtained results.


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