Global asymptotic stability and global exponential stability ofcontinuous-time recurrent neural networks

Sanqing Hu; Jun Wang

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Global asymptotic stability and global exponential stability ofcontinuous-time recurrent neural networks

机译：连续时间递归神经网络的全局渐近稳定性和全局指数稳定性

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This paper presents new results on global asymptotic stability (GAS) and global exponential stability (GES) of a general class of continuous-time recurrent neural networks with Lipschitz continuous and monotone nondecreasing activation functions. We first give three sufficient conditions for the GAS of neural networks. These testable sufficient conditions differ from and improve upon existing ones. We then extend an existing GAS result to GES one and also extend the existing GES results to more general cases with less restrictive connection weight matrices and/or partially Lipschitz activation functions

机译：本文介绍了具有Lipschitz连续和单调非递减激活函数的一类通用连续时间递归神经网络的全局渐近稳定性（GAS）和全局指数稳定性（GES）的新结果。我们首先给出神经网络GAS的三个充分条件。这些可测试的充分条件与现有条件有所不同并有所改进。然后，我们将现有的GAS结果扩展到GES，并将现有的GES结果扩展到连接权重矩阵限制较小和/或部分Lipschitz激活函数的更一般的情况

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《IEEE Transactions on Automatic Control》 |2002年第5期|p.802-807|共6页
作者
Sanqing Hu; Jun Wang;
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正文语种 eng
中图分类自动化系统;
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absolute stability; asymptotic stability; continuous time systems; recurrent neural nets; Lipschitz continuous activation functions; connection weight matrices; continuous-time recurrent neural networks; global asymptotic stability; global exponential stability;

机译：绝对稳定性渐近稳定性连续时间系统递归神经网络Lipschitz连续激活函数连接权矩阵连续时间递归神经网络全局渐近稳定性全局指数稳定性;

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Global asymptotic stability and global exponential stability ofcontinuous-time recurrent neural networks

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