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Adaptive Synchronization for a Class of Uncertain Fractional-Order Neural Networks

机译:一类不确定分数阶神经网络的自适应同步

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In this paper, synchronization for a class of uncertain fractional-order neural networks subject to external disturbances and disturbed system parameters is studied. Based on the fractional-order extension of the Lyapunov stability criterion, an adaptive synchronization controller is designed, and fractional-order adaptation law is proposed to update the controller parameter online. The proposed controller can guarantee that the synchronization errors between two uncertain fractional-order neural networks converge to zero asymptotically. By using some proposed lemmas, the quadratic Lyapunov functions are employed in the stability analysis. Finally, numerical simulations are presented to confirm the effectiveness of the proposed method.
机译:本文研究了一类不确定的分数阶神经网络在外部干扰和系统参数干扰下的同步性。基于Lyapunov稳定性准则的分数阶扩展,设计了一种自适应同步控制器,并提出了分数阶自适应律,以在线更新控制器参数。所提出的控制器可以保证两个不确定的分数阶神经网络之间的同步误差渐近收敛到零。通过使用一些提出的引理,在稳定性分析中采用了二次Lyapunov函数。最后,通过数值模拟验证了所提方法的有效性。

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