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Robust control of a class of neural networks with bounded uncertainties and time-varying delays

机译:一类具有有限不确定性和时变时滞的神经网络的鲁棒控制

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This paper investigates the robust control problem for a class of neural networks subject to bounded uncertainties and time-varying delays. A memoryless decentralized variable structure control law with dead-zone input for guaranteeing global asymptotical system stability is derived. The results demonstrate that the derived control law does not restrict the derivative of the time-varying delays even if dead-zone nonlinearity occurs in the control input. Such a control law can be used to stabilize Cohen-Grossberg neural networks, cellular neural networks and Hopfield neural networks; all of which have bounded uncertainties and time-varying delays. Two examples are provided to illustrate the effectiveness and validity of the proposed control scheme.
机译:本文研究了一类具有有限不确定性和时变时滞的神经网络的鲁棒控制问题。推导了具有死区输入的无记忆分散式变结构控制律,以保证全局渐近系统的稳定性。结果表明,即使在控制输入中出现死区非线性,导出的控制律也不会限制时变延迟的导数。这样的控制定律可以用来稳定Cohen-Grossberg神经网络,细胞神经网络和Hopfield神经网络。所有这些都限制了不确定性和随时间变化的延迟。提供了两个示例来说明所提出的控制方案的有效性和有效性。

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