AIMS Mathematics, 2020, 5(6): 5955-5968. doi: 10.3934/math.2020381.

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New convergence on inertial neural networks with time-varying delays and continuously distributed delays

1 College of Mathematics and Physics, Hunan University of Arts and Science, Changde, 415000, Hunan, P. R. China
2 School of Mathematics and Statistics, Changsha University of Science and Technology; Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha 410114, Hunan, P. R. China

In this paper, a class of inertial neural networks with bounded time-varying delays and unbounded continuously distributed delays are explored by applying non-reduced order method. Based upon differential inequality techniques and Lyapunov function method, a new sufficient condition is presented to ensure all solutions of the addressed model and their derivatives converge to zero vector, which refines some previously known researches. Moreover, a numerical example is provided to illustrate these analytical conclusions.
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Keywords inertial neural networks; time-varying delay; distributed delay; global convergence

Citation: Qian Cao, Xin Long. New convergence on inertial neural networks with time-varying delays and continuously distributed delays. AIMS Mathematics, 2020, 5(6): 5955-5968. doi: 10.3934/math.2020381

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