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On the robustness of global exponential stability for hybrid neural networks with noise and delay perturbations

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成果类型:
期刊论文
作者:
Jiang, Feng*;Yang, Hua;Shen, Yi
通讯作者:
Jiang, Feng
作者机构:
[Jiang, Feng] Zhongnan Univ Econ & Law, Sch Math & Stat, Wuhan 430073, Peoples R China.
[Yang, Hua] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
[Shen, Yi] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Key Lab, Minist Educt Image Proc & Intelligent Control, Wuhan 430074, Peoples R China.
通讯机构:
[Jiang, Feng] Z
Zhongnan Univ Econ & Law, Sch Math & Stat, Wuhan 430073, Peoples R China.
语种:
英文
关键词:
Neural networks;Delay;Delay perturbation;Global exponential stability;Globally exponentially stable;Hybrid neural networks;Noise;Noise intensities;Time varying networks
期刊:
Neural Computing and Applications
ISSN:
0941-0643
年:
2014
卷:
24
期:
7-8
页码:
1497-1504
基金类别:
The authors would like to thank Prof. John MacIntyre and anonymous referees for their constructive suggestions and comments. The work is supported by the Research Fund for Wuhan Polytechnic University under Grant 2012Y16, the Fundamental Research Funds for the Central Universities, the China Postdoctoral Science Foundation under Grant 2012M511615, and the State Key Program of National Natural Science of China under Grant 61134012.
机构署名:
本校为其他机构
院系归属:
数学与计算机学院
摘要:
The paper is concerned with the robustness of global exponential stability of hybrid neural networks subject to noise and delay simultaneously. Given a globally exponentially stable hybrid neural network, the aim of the paper is to characterize how much delay and noise intensity hybrid neural networks can bear such that the perturbed hybrid neural network remains globally exponentially stable, in the presence of delay and noise simultaneousl...

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