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Global existence of periodic solutions of BAM neural networks with variable coefficients

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成果类型:
期刊论文
作者:
Guo, SJ*;Huang, LH;Dai, BX;Zhang, ZZ
通讯作者:
Guo, SJ
作者机构:
[Guo, SJ] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China.
Hunan City Univ, Dept Math, Yiyang 413000, Hunan, Peoples R China.
通讯机构:
[Guo, SJ] H
Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China.
语种:
英文
关键词:
Periodic solution;BAM neural networks;Coincidence degree
期刊:
Physics Letters A
ISSN:
0375-9601
年:
2003
卷:
317
期:
1-2
页码:
97-106
机构署名:
本校为其他机构
院系归属:
理学院
摘要:
In this Letter, we study BAM (bidirectional associative memory) networks with variable coefficients. By some spectral theorems and a continuation theorem based on coincidence degree, we not only obtain some new sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the periodic solution but also estimate the exponentially convergent rate. Our results are less restrictive than previously known criteria and can be applied to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity. Moreover, these...

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