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Existence and global exponential stability of periodic solution to BAM neural networks with periodic coefficients and continuously distributed delays

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成果类型:
期刊论文
作者:
Zhou, TJ;Chen, AP*;Zhou, YY
通讯作者:
Chen, AP
作者机构:
[Chen, AP] Xiangtan Univ, Dept Math, Chenzhou 423000, Peoples R China.
Cent S Univ, Sch Math, Changsha 410083, Peoples R China.
Hunan Agr Univ, Sci Coll, Changsha 410128, Peoples R China.
通讯机构:
[Chen, AP] X
Xiangtan Univ, Dept Math, Chenzhou 423000, Peoples R China.
语种:
英文
关键词:
BAM neural networks;periodic solution;global exponential stability;continuously distributed delays
期刊:
Physics Letters A
ISSN:
0375-9601
年:
2005
卷:
343
期:
5
页码:
336-350
机构署名:
本校为其他机构
院系归属:
理学院
摘要:
By using the continuation theorem of coincidence degree theory and Liapunov function, we obtain some sufficient criteria to ensure the existence and global exponential stability of periodic solution to the bidirectional associative memory (BAM) neural networks with periodic coefficients and continuously distributed delays. These results improve and generalize the works of papers [J. Cao, L. Wang, Phys. Rev. E 61 (2000) 1825] and [Z. Liu, A. Chen, J. Cao, L. Huang, IEEE Trans. Circuits Systems 150 (2003) 1162]. An example is given to illustrate that ...

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