Global exponential stability of Clifford-valued recurrent neural networks

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• 作者：Jingwen Zhu^a ; ^bAuthor Vitae ; Jitao Sun^a ; ^{sunjt@sh163.net" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Clifford-valued ; Recurrent neural network ; Variation parameter ; Exponential stability
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：173
• 期：part_P3
• 页码：685-689
• 全文大小：430 K

文摘

This paper investigates global exponential stability of a class of Clifford-valued recurrent neural networks. By using Brouwer's fixed point theorem, the existence of the equilibrium point of Clifford-valued recurrent neural networks is studied. A sufficient condition of global exponential stability is given by the method of the Clifford-valued variation parameter and inequality technique. Compared with the previous methods, our method does not resort to any Lyapunov function which is not easy to construct.