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Synchronization Control of Quaternion-Valued Neural Networks with Parameter Uncertainties

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Abstract

In this paper, by starting from basic quaternion algebra properties and algorithms, we develop a comprehensive set of properties to ensure the uncertain quaternion-valued neural networks can receive synchronization and quasi-synchronization goals. By endowing the classic Lyapunov technique, several sufficient criteria for the synchronization and quasi-synchronization analysis of the addressed model are proposed by means of two simple and rigorous control strategies. Particularly, lexicographical ordering approach is proposed in this paper, which can be employed to determine the “magnitude” of two different quaternion-valued. Finally, we have numerical evidences that the mathematical model and the conclusions presented are validate.

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Authors and Affiliations

  1. School of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062, China

    Hongzhi Wei, Baowei Wu & Ruoxia Li

Authors
  1. Hongzhi Wei
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  2. Baowei Wu
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  3. Ruoxia Li
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Correspondence to Hongzhi Wei.

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This work was jointly supported by the National Natural Science Foundation of China under Grant No. 61803247, Project Funded by China Postdoctoral Science Foundation under Grant No. 2018M640948, the Fundamental Research Funds for the Central Universities under Grant No. GK201903003, Shaanxi Postdoctoral Science Foundation under Grant No. 2018BSHEDZZ129.

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Wei, H., Wu, B. & Li, R. Synchronization Control of Quaternion-Valued Neural Networks with Parameter Uncertainties. Neural Process Lett 51, 1465–1484 (2020). https://doi.org/10.1007/s11063-019-10153-2

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