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Synchronization of fuzzy bidirectional associative memory neural networks with various time delays

机译:具有多种时滞的模糊双向联想记忆神经网络的同步

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摘要

The problem of synchronization for fuzzy bidirectional associative memory (BAM) neural networks (NNs) with various time delays is formulated and investigated. The various delays consist of discrete delays, unbounded distributed delays and constant delay in the leakage term (i.e. 'leakage delay') are considered. Then, some sufficient conditions are presented to guarantee the global asymptotic stability of the error dynamical system by using Lyapunov-Krasovskii functional and linear matrix inequality (LMI) method. As a result, we achieved synchronization of master-slave (drive-response) fuzzy BAM NNs systems. Even, if there is no leakage delay, the obtained results are less restrictive to some known sufficient conditions. Moreover, the proposed results do not require the boundedness, differentiability and monotonicity of the activation functions, which can also be easily checked via the LMI solver in robust control toolbox in Matlab. Furthermore, using a parameter-dependent Lyapunov function approach the synchronization problem for polytopic uncertain fuzzy BAM NNs with various time delays is considered. The parameter uncertainties under consideration are assumed to belong to a fixed convex polytope. Finally, two numerical examples and simulations are given to illustrate the effectiveness of the derived theoretical results. (C) 2015 Elsevier Inc. All rights reserved.
机译:提出并研究了具有多种时滞的模糊双向联想记忆(BAM)神经网络(NNs)的同步问题。各种延迟包括离散延迟,无界分布延迟和泄漏项中的恒定延迟(即“泄漏延迟”)。然后,通过使用Lyapunov-Krasovskii泛函和线性矩阵不等式(LMI)方法,给出了一些充分的条件来保证误差动力系统的全局渐近稳定性。结果,我们实现了主-从(驱动响应)模糊BAM NNs系统的同步。即使没有泄漏延迟,所获得的结果对某些已知充分条件的限制也较小。此外,所提出的结果不需要激活函数的有界性,可微性和单调性,也可以通过Matlab健壮的控制工具箱中的LMI求解器轻松地对其进行检查。此外,考虑使用参数依赖的Lyapunov函数方法,研究了具有各种时间延迟的多义不确定BAM神经网络的同步问题。假定所考虑的参数不确定性属于固定的凸多面体。最后,通过两个数值例子和仿真来说明所导出理论结果的有效性。 (C)2015 Elsevier Inc.保留所有权利。

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