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Weighted Pseudo Almost-Automorphic Solutions of Quaternion-Valued RNNs With Mixed Delays

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Abstract

This work deals with a nonlinear differential equation for a quaternion-valued recurrent neural network. By using the contraction mapping principle and some differential inequalities, we directly studied the existence and the global exponential stability of weighted pseudo-almost automorphic solution for this class of quaternion-valued neural networks. Here, methods were applied without a real or a complex decomposition of the equation system. In addition, an application verifying our results and its numerical simulation was given. The generated results about the weighted pseudo-almost automorphic solution of the considered model are new.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their constructive comments, which greatly improved the quality of the original version.

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Mathematics, Faculty of Sciences Van Yuzuncu Yil University, 65080, Campus, Van, Turkey

    Ramazan Yazgan

  2. Department of Mathematics, Higher School of Sciences and Technology of Hammam Sousse, University of Sousse, Hammam sousse, 4011, State, Tunisia

    Salsabil Hajjaji

  3. Department of Computer Science, Laboratory of Math Physics Specials Functions and Applications, LR11ES35, Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, 4002, Tunisia

    Farouk Chérif

Authors
  1. Ramazan Yazgan
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  2. Salsabil Hajjaji
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  3. Farouk Chérif
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Correspondence to Salsabil Hajjaji.

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Yazgan, R., Hajjaji, S. & Chérif, F. Weighted Pseudo Almost-Automorphic Solutions of Quaternion-Valued RNNs With Mixed Delays. Neural Process Lett 55, 423–440 (2023). https://doi.org/10.1007/s11063-022-10890-x

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