School of Mathematics and Computation Science, Anqing Normal University, Anqing 246133, China.
School of Mathematics, Southeast University, Nanjing 210096, China.
Neural Netw. 2019 Feb;110:186-198. doi: 10.1016/j.neunet.2018.12.004. Epub 2018 Dec 12.
This paper considers the global asymptotical synchronization of fractional-order memristive complex-valued neural networks (FOMCVNN), with both parameter uncertainties and multiple time delays. Sufficient conditions of uncertain FOMCVNN, with multiple time delays, are established through the employment of comparison principle and Lyapunov direct method. A numerical example is used to show the effectiveness of the proposed methods.
本文考虑了具有参数不确定性和多个时滞的分数阶忆阻复值神经网络（FOMCVNN）的全局渐近同步。通过比较原理和李雅普诺夫直接方法的应用，建立了具有多个时滞的不确定分数阶忆阻复值神经网络的充分条件。通过数值实例验证了所提出方法的有效性。
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