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pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays

机译:随机映像基于人的双向关联存储器(BAM)神经网络的PTH矩指数稳定性

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摘要

Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Ito's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. (C) 2017 Elsevier Ltd. All rights reserved.
机译:基于随机映射器的双向关联存储器(BAM)具有时间延迟的神经网络在神经网络系统的设计和实现中起着越来越重要的作用。 在Filippov解决方案的框架下,研究了随机映像基于基于随机映射器的BAM神经网络的第P型幂稳定性的问题。 通过使用随机稳定性理论,ITO的差分公式和年轻不等式,衍生标准。 同时,随着Lyapunov的方法和Cauchy-Schwarz不等式,我们为上述系统的平均方形指数稳定性推出了一些足够的条件。 所获得的结果改善并延长了基于Memristor的或通常的神经网络动态系统的工作。 提供了四个数值例子以说明所提出的结果的有效性。 (c)2017 Elsevier Ltd.保留所有权利。

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