具有时变延迟和马尔可夫切换的递归神经网络的输入状态稳定性

2012 24th Chinese Control and Decision Conference (CCDC) Pub Date : 2012-05-23 DOI:10.1109/CCDC.2012.6243023

Yong Xu, Song Zhu

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引用次数: 0

摘要

本文给出了具有马尔可夫切换的递归神经网络输入到状态稳定性的一个代数判据。该准则易于用连接权值进行验证。数值算例验证了所提准则的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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2012 24th Chinese Control and Decision Conference (CCDC)

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