Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks

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• 作者：Zhengwen Tu^a ; ^b ; Jinde Cao^a ; ^c ; ^{jdcao@seu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Tasawar Hayat^c ; ^d
• 关键词：Inertial neural networks ; Dissipativity ; Uncertainty ; Matrix measure
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：75
• 期：Complete
• 页码：47-55
• 全文大小：716 K

文摘

The present paper is devoted to investigating the global dissipativity for inertial neural networks with time-varying delays and parameter uncertainties. By virtue of a suitable substitution, the original system is transformed to the first order differential system. By means of matrix measure, generalized Halanay inequality, and matrix-norm inequality, several sufficient criteria for the global dissipativity of the addressed neural networks are proposed. Meanwhile, the specific estimations of positive invariant sets and globally attractive sets are obtained. Finally, two examples are provided to validate our theoretical results.