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Adaptive synchronization in an array of chaotic neural networks with mixed delays and jumping stochastically hybrid coupling

机译:混合时滞和跳跃随机混杂耦合的混沌神经网络阵列的自适应同步。

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In this paper, a general model of an array of N linearly coupled delayed neural networks with Markovian jumping hybrid coupling is introduced. The hybrid coupling consists of constant coupling, discrete and distributed time-varying delay coupling. The complex dynamical network jumps from one mode to another according to a Markovian chain, where all the coupling configurations are also dependent on mode switching. Meanwhile, all the coupling terms are subjected to stochastic disturbances which are described in terms of a Brownian motion. By adaptive approach, some sufficient criteria have been derived to ensure the synchronization in an array of jump neural networks with mixed delays and hybrid coupling in mean square. Surprisingly, it is found that complex networks with two different structure can also be synchronized according to known probability matrix. Finally, an example illustrated by switching between small-world networks and nearest-neighbor networks is given to show the effectiveness of the proposed criteria.
机译:本文介绍了具有马尔可夫跳跃混合耦合的N个线性耦合时滞神经网络阵列的通用模型。混合耦合包括恒定耦合,离散和分布式时变延迟耦合。复杂的动力学网络根据马尔可夫链从一种模式跳到另一种模式,其中所有耦合配置也都依赖于模式切换。同时,所有耦合项都受到随机扰动的影响,用布朗运动来描述。通过自适应方法,已经导出了一些足够的标准,以确保具有混合延迟和均方混合耦合的跳跃神经网络阵列中的同步。令人惊讶地,发现具有两种不同结构的复杂网络也可以根据已知的概率矩阵进行同步。最后,给出了一个在小世界网络和最近邻居网络之间切换的示例,以显示所提出标准的有效性。

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