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Almost Automorphic Solutions in Distribution Sense of Quaternion-Valued Stochastic Recurrent Neural Networks with Mixed Time-Varying Delays

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Abstract

In this paper, we consider quaternion-valued stochastic recurrent neural networks with mixed time-varying delays by direct method. Base on the Banach fixed point theorem and stochastic analysis techniques, we obtain some sufficient conditions for the existence and global exponential stability of almost automorphic solutions in distribution sense for the neural networks. As special cases of our results, we also obtain the results about the existence and global exponential stability of almost automorphic solutions in distribution sense for complex-valued and real-valued stochastic neural networks. All of these results are new. Finally, we give numerical examples and simulations to illustrate the feasibility of our results.

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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

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  1. Yongkun Li
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  2. Xiaofang Meng
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Correspondence to Yongkun Li.

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This work is supported by the National Natural Sciences Foundation of People’s Republic of China under Grants No. 11861072 and No. 11361072.

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Li, Y., Meng, X. Almost Automorphic Solutions in Distribution Sense of Quaternion-Valued Stochastic Recurrent Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 51, 1353–1377 (2020). https://doi.org/10.1007/s11063-019-10151-4

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