Input-to-state stability of memristor-based complex-valued neural networks with time delays

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• 作者：Dan LiuAuthor Vitae ; Song Zhu ; ^{songzhu82@gmail.com}Author Vitae ; Wenting ChangAuthor Vitae
• 关键词：Complex-valued neural networks ; Memristor ; Input-to-state stability ; Time delays
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：19 January 2017
• 年：2017
• 卷：221
• 期：Complete
• 页码：159-167
• 全文大小：884 K
• 卷排序：221

文摘

The paper concentrates on the input-to-state stability of memristor-based complex-valued neural networks with time delays. Several new stability criteria are proposed by utilizing the Lyapunov function, differential inclusions theory and set-valued maps. The obtained results generalize some existing literature about real-valued neural networks as special conditions.