具有脉冲效应的中立型混沌马尔可夫神经网络的随机同步

Chengde Zheng, Xixi Lv
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引用次数: 0

摘要

研究了一类具有马尔可夫跳变参数的中立型混沌神经网络在脉冲扰动下的全局随机同步问题。利用驱动响应概念和时滞反馈控制技术,利用Lyapunov泛函方法、Jensen积分不等式、一种新的互反凸引理和自由权矩阵方法,导出了两个具有脉冲扰动的马尔可夫跳变混沌延迟神经网络渐近同步的一个新的充分条件。所提出的结果不需要激活函数的可微性和单调性,可以很容易地通过Matlab软件进行验证。最后,通过数值仿真验证了所提同步方案的有效性。
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Stochastic Synchronization of Neutral-Type Chaotic Markovian Neural Networks with Impulsive Effects
This paper studies the globally stochastic synchronization problem for a class of neutral-type chaotic neural networks with Markovian jumping parameters under impulsive perturbations. By virtue of drive-response concept and time-delay feedback control techniques, by using the Lyapunov functional method, Jensen integral inequality, a novel reciprocal convex lemma and the free-weight matrix method, a novel sufficient condition is derived to ensure the asymptotic synchronization of two identical Markovian jumping chaotic delayed neural networks with impulsive perturbation. The proposed results, which do not require the differentiability and monotonicity of the activation functions, can be easily checked via Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.
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