首页> 外文期刊>Modern Physics Letters, B. Condensed Matter Physics, Statistical Physics, Applied Physics >ADAPTIVE SYNCHRONIZATION FOR UNKNOWN STOCHASTIC CHAOTIC NEURAL NETWORKS WITH MIXED TIME-DELAYS BY OUTPUT COUPLING
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ADAPTIVE SYNCHRONIZATION FOR UNKNOWN STOCHASTIC CHAOTIC NEURAL NETWORKS WITH MIXED TIME-DELAYS BY OUTPUT COUPLING

机译:基于输出耦合的混合时滞未知随机混沌神经网络的自适应同步

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摘要

This paper is concerned with the synchronization problem for a class of stochastic neural networks with unknown parameters and mixed time-delays via output coupling. The mixed time-delays comprise the time-varying delay and distributed delay, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. Firstly, we use Lyapunov functions to establish general theoretical conditions for designing the output coupling matrix. Secondly, by using the adaptive feedback technique, a simple, analytical and rigorous approach is proposed to synchronize the stochastic neural networks with unknown parameters and mixed time-delays. Finally, numerical simulation results are given to show the effectiveness of the proposed method.
机译:本文研究了一类参数未知且混合时滞通过输出耦合的随机神经网络的同步问题。混合时延包括时变时延和分布式时延,并且神经网络受到随机干扰的影响,以布朗运动来描述。首先,我们使用李雅普诺夫函数建立设计输出耦合矩阵的一般理论条件。其次,通过使用自适应反馈技术,提出了一种简单,分析和严格的方法来同步具有未知参数和混合时滞的随机神经网络。最后,数值仿真结果表明了该方法的有效性。

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