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Globally Attractive Periodic State of Discrete-Time Cellular Neural Networks with Time-Varying Delays

机译:具有时变延迟的离散时间蜂窝神经网络的全球有吸引力的周期性状态

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For the convenience of computer simulation, the discrete-time systems in practice are often considered. In this paper, Discrete-time cellular neural networks (DTCNNs) are formulated and studied in a regime where they act as a switchboard for oscillating inputs. Several sufficient conditions are obtained to ensure DTCNNs with delays have a periodic orbit and this periodic orbit is globally attractive using a method based on the inequality method and the contraction mapping principle. Finally, simulations results are also discussed via one illustrative example.
机译:为了便于计算机仿真,通常考虑实践中的离散时间系统。在本文中,在它们用作用于振荡输入的切换器的状态下配制并研究了离散时间蜂窝神经网络(DTCNNS)。获得几种充分的条件以确保具有延迟的DTCNN具有周期性轨道,并且使用基于不等式方法和收缩映射原理的方法,该周期性轨道是全球吸引力。最后,还通过一个说明性示例讨论了模拟结果。

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