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Input-to-state stability of recurrent neural networks with time-varying delays and Markovian switching

机译:具有时变时滞和马尔可夫切换的递归神经网络的输入状态稳定性

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摘要

This paper presents an algebraic criterion for the input-to-state stability (ISS) of recurrent neural networks with Markovian switching. The criterion is easy to be verified with the connection weights. A numerical example is given to demonstrate the effectiveness of the proposed criteria.
机译:本文提出了具有马尔可夫切换的递归神经网络输入状态稳定性(ISS)的代数准则。该标准很容易通过连接权重进行验证。数值例子说明了所提出标准的有效性。

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