Global attractivity of memristor-based fractional-order neural networks

详细信息    查看全文

• 作者：Shuo ZhangAuthor Vitae ; Yongguang Yu ; ^{ygyu@bjtu.edu.cn}Author Vitae ; Yajuan GuAuthor Vitae
• 关键词：Fractional-order neural networks ; Memristor-based ; Non-Lipschitz neuron activations ; Attractivity ; Filippov solution
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：1 March 2017
• 年：2017
• 卷：227
• 期：Complete
• 页码：64-73
• 全文大小：1020 K
• 卷排序：227

文摘

Some dynamical properties, especially the global attractivity, of memristor-based fractional-order neural networks (FNN) are discussed. By using Filippov solutions, the existence of memristor-based FNN's solutions is firstly guaranteed under a growth condition. With non-Lipschitz neuron activations, different dynamics of memristor-based FNN are analyzed by employing the Lyapunov functionals. Then, a local Mittag-Leffler stability condition is presented for memristor-based FNN. To obtain the global dynamical properties, the global boundedness of memristor-based FNN is discussed. Further, with proposing additional conditions, the global attractivity of memristor-based FNN is realized. To verify the effectiveness of the obtained results, three numerical examples are given in the end.