Extended dissipative analysis for memristive neural networks with two additive time-varying delay components

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• 作者：Hongzhi Wei^a ; ^{hongzhiwei0922@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Ruoxia Li^b ; ^{ruoxiali1227@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chunrong Chen^aAuthor Vitae ; Zhengwen Tu^cAuthor Vitae
• 关键词：Memristor ; Extended dissipative ; Quadratically stability ; Additive time-varying delays ; Linear matrix inequalities (LMIs)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 December 2016
• 年：2016
• 卷：216
• 期：Complete
• 页码：429-438
• 全文大小：1015 K

文摘

This paper concentrates on the extended dissipativity of memristive neural networks with two additive time-varying delays. After giving a foundation to the memristive model, the paper establishes some fundamental results on quadratically stability and extended dissipativity criteria by means of the Lyapunov functional, integral inequality, as well as the relationship between time-varying delays. The novel extended dissipative inequality contains several weighting matrices, by converting the weighting matrices in a new performance index, the extended dissipativity will be degraded to the H_∞$H_{\infty }$ performance, L₂−L_∞$L_2-L_{\infty }$ performance, passivity and dissipativity, respectively. Finally, one example is given to substantiate the significant improvement of the theoretical approaches.