4.7 Article Proceedings Paper

Non-separation method-based robust finite-time synchronization of uncertain fractional-order quaternion-valued neural networks

期刊

APPLIED MATHEMATICS AND COMPUTATION
卷 409, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2021.126377

关键词

Robust finite-time synchronization; Uncertain parameters; Fractional-order; Quaternion-valued neural networks

资金

  1. Tianshan Youth Program-Training Program for Excellent Young Scientific and Technological Talents [2019Q017]
  2. National Natural Science Foundation of China [11702237]

向作者/读者索取更多资源

This paper addresses the robust finite-time synchronization issue for a class of uncertain fractional-order quaternion-valued neural networks. A general fractional differential inequality is developed to provide new insight into the research about finite-time stability and synchronization of fractional-order systems. Through the newly developed inequality and quaternion inequality techniques, easily-verified algebraic criteria for robust F-TS are established, with explicitly reckoned settling time.
In this paper, robust finite-time synchronization (F-TS) issue is addressed for a class of uncertain fractional-order quaternion-valued neural networks by employing non-separation method instead of separation method. First, a general fractional differential inequality is developed to provide new insight into the research about finite-time stability and synchronization of fractional-order systems. Next, quaternion-valued feedback controller and quaternion-valued adaptive controller are designed. On the basis of the newly developed inequality, quaternion inequality techniques, together with the properties of fractional calculus and reduction to absurdity, some easily-verified algebraic criteria for robust F-TS are established, and the settling time for robust F-TS is explicitly reckoned, which depends on not only the controller parameters but also the initial values and order of the considered systems. Eventually, numerical results are provided to substantiate our robust F-TS criteria. (C) 2021 Elsevier Inc. All rights reserved.

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