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Existence and global exponential stability of periodic solution for discrete-time BAM neural networks

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成果类型:
期刊论文
作者:
Zhou, Tiejun*;Liu, Yirong;Liu, Yuehua
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.
语种:
英文
关键词:
BAM neural networks;periodic solution;global exponential stability;discrete-time analogues;BIDIRECTIONAL ASSOCIATIVE MEMORY;DELAYS;COEFFICIENTS
期刊:
Applied Mathematics and Computation
ISSN:
0096-3003
年:
2006
卷:
182
期:
2
页码:
1341-1354
基金类别:
This work is supported in part by the Scientific Research Foundation of Hunan Provincial Education Department under Grant 05C309, and in part by the Science Foundation of the Hunan Agricultural University under Grant 2005WD15.
机构署名:
本校为第一且通讯机构
院系归属:
理学院
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 stabil...

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