Existence and global exponential stability of periodic solution for discrete-time BAM neural networks
作者：
Zhou, Tiejun*;Liu, Yirong;Liu, Yuehua
期刊：
Applied Mathematics and Computation,2006年182(2):1341-1354 ISSN：0096-3003
通讯作者：
Zhou, Tiejun
作者机构：
[Zhou, Tiejun] Hunan Agr Univ, Coll Sci, Changsha 410128, Peoples R China.;Cent S Univ, Sch Math, Changsha 410000, Peoples R China.
通讯机构：
[Zhou, Tiejun] H;Hunan Agr Univ, Coll Sci, Changsha 410128, Peoples R China.
关键词：
BAM neural networks;periodic solution;global exponential stability;discrete-time analogues;BIDIRECTIONAL ASSOCIATIVE MEMORY;DELAYS;COEFFICIENTS
摘要：
The discrete-time analogues of bidirectional associative memory neural networks with periodic coefficients and distributed delays are formulated and studied. And by using the continuation theorem of coincidence degree theory, we derive the existence of periodic solution for the discrete-time BAM neural networks. And by constructing a appropriate Lyapunov-type sequence, we prove the global exponential stability of the periodic solution for the model. It is shown that the discrete-time analogues inherit the existence and global exponential stability of periodic solution for the continuous-time BAM neural networks. An example is given to illustrate the effectiveness of the obtained results. © 2006 Elsevier Inc. All rights reserved.
语种：
英文
展开
