Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales

被引:0
|
作者
Yongkun Li
Xiaofang Meng
Lianglin Xiong
机构
[1] Yunnan University,Department of Mathematics
[2] Yunnan Minzu University,School of Mathematics and Computer Science
来源
International Journal of Machine Learning and Cybernetics | 2017年 / 8卷
关键词
Hopfield neural networks; Mixed time-varying delays; Leakage delays; Pseudo almost periodic solutions; Time scales;
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学科分类号
摘要
We propose a class of neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales. Applying the exponential dichotomy of linear dynamic equations on time scales, Banach’s fixed point theorem and theory of calculus on time scales, we obtain several sufficient conditions to ensure the existence and global exponential stability of pseudo almost periodic solutions of the proposed neural networks. Finally, we illustrate the effectiveness of the obtained results with an example. The example also shows that the continuous-time neural network and its discrete-time analogue have the same dynamical behaviors when considering the pseudo almost periodicity.
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页码:1915 / 1927
页数:12
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