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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2017

22.07.2016 | Original Article

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

verfasst von: Yongkun Li, Xiaofang Meng, Lianglin Xiong

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2017

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Abstract

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.
Vorheriger Artikel A projected-based neural network method for second-order cone programming
Nächster Artikel Predicting run time of classification algorithms using meta-learning

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Metadaten
Titel
Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales
verfasst von
Yongkun Li
Xiaofang Meng
Lianglin Xiong
Publikationsdatum
22.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2017
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-016-0570-7

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