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

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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.

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Authors and Affiliations

  1. Department of Mathematics, Yunnan University, Kunming, 650091, Yunnan, People’s Republic of China

    Yongkun Li & Xiaofang Meng

  2. School of Mathematics and Computer Science, Yunnan Minzu University, Kunming, 650500, Yunnan, People’s Republic of China

    Lianglin Xiong

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  1. Yongkun Li
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  2. Xiaofang Meng
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  3. Lianglin Xiong
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Correspondence to Yongkun Li.

Additional information

This work is supported by the National Natural Sciences Foundation of People’s Republic of China under Grants 11361072 and 11461082.

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Li, Y., Meng, X. & Xiong, L. Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales. Int. J. Mach. Learn. & Cyber. 8, 1915–1927 (2017). https://doi.org/10.1007/s13042-016-0570-7

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