Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays

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• 作者：Hongfei Li ; Haijun Jiang ; ^{jianghaijunxju@163.com" class="auth_mail" title="E-mail the corresponding author} ; Cheng Hu
• 关键词：Memristor ; Periodic solution ; Boundedness ; BAM neural networks ; Global exponential stability
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：75
• 期：Complete
• 页码：97-109
• 全文大小：495 K

文摘

In this paper, we investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, boundedness and ultimate boundedness of solutions of memristor-based BAM neural networks are guaranteed by Chain rule and inequalities technique. Moreover, a new method involving Yoshizawa-like theorem is favorably employed to acquire the existence of periodic solution. By applying the theory of set-valued maps and functional differential inclusions, an available Lyapunov functional and some new testable algebraic criteria are derived for ensuring the uniqueness and global exponential stability of periodic solution of memristor-based BAM neural networks. The obtained results expand and complement some previous work on memristor-based BAM neural networks. Finally, a numerical example is provided to show the applicability and effectiveness of our theoretical results.