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带有跳跃模式相关离散和无界分布时滞的耦合中立型神经网络的同步。

Synchronization of Coupled Neutral-Type Neural Networks With Jumping-Mode-Dependent Discrete and Unbounded Distributed Delays.

出版信息

IEEE Trans Cybern. 2013 Feb;43(1):102-14. doi: 10.1109/TSMCB.2012.2199751. Epub 2012 Jun 25.

Abstract

In this paper, the synchronization problem is studied for an array of N identical delayed neutral-type neural networks with Markovian jumping parameters. The coupled networks involve both the mode-dependent discrete-time delays and the mode-dependent unbounded distributed time delays. All the network parameters including the coupling matrix are also dependent on the Markovian jumping mode. By introducing novel Lyapunov-Krasovskii functionals and using some analytical techniques, sufficient conditions are derived to guarantee that the coupled networks are asymptotically synchronized in mean square. The derived sufficient conditions are closely related with the discrete-time delays, the distributed time delays, the mode transition probability, and the coupling structure of the networks. The obtained criteria are given in terms of matrix inequalities that can be efficiently solved by employing the semidefinite program method. Numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.

摘要

本文研究了具有马尔可夫跳跃参数的 N 个相同延迟中立型神经网络的同步问题。所涉及的耦合网络既有模式相关的离散时间延迟,也有模式相关的无界分布时间延迟。包括耦合矩阵在内的所有网络参数都依赖于马尔可夫跳跃模式。通过引入新的李雅普诺夫-克拉索夫斯基泛函并运用一些分析技术,推导出了保证耦合网络在均方意义下渐近同步的充分条件。所得到的充分条件与离散时间延迟、分布式时间延迟、模式转移概率以及网络的耦合结构密切相关。所得准则以矩阵不等式的形式给出,可通过半定规划方法有效地求解。数值仿真进一步验证了所提出方法的有效性。

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