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Finite-Time Projective Synchronization of Stochastic Complex-Valued Neural Networks with Time Varying Delays

机译:随机复合性神经网络随时间变化延迟的有限时间投影同步

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Complex-valued neural networks (CVNNs) contain complex-valued parameters and variables, which is more effective when dealing with complex signals. In order to extend and complement the known results of CVNNs, in this paper, the problem of finite-time projective synchronization is explored for a class of stochastic complex-valued neural networks (SCVNNs) with time-varying delays. Based on the Lyapunov stability approach and inequalities techniques, some novel projective synchronization criteria are established by decomposing SCVNNs into real and imaginary parts. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed control scheme and the obtained theoretical results.
机译:复合值的神经网络(CVNNS)包含复值的参数和变量,在处理复杂信号时更有效。 为了延长和补充CVNN的已知结果,在本文中,探讨了一类随机复合值神经网络(SCVNNS)的有限时间投影同步的问题,其时变延迟。 基于Lyapunov稳定性方法和不平等技术,通过将SCVNN分解成真实和虚部的部分来建立一些新的投影同步标准。 最后,提出了一种数值模拟以证明所提出的控制方案和所获得的理论结果的有效性。

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