Fixed-time synchronization for delayed inertial complex-valued neural networks

Changqing Long, Guodong Zhang and Junhao Hu

Applied Mathematics and Computation, 2021, vol. 405, issue C

Abstract: This paper researches the problem of p-norm fixed-time synchronization for a class of delayed inertial complex-valued neural networks (ICVNNs). By using reduced-order transformation and separating real and imaginary parts of complex-valued parameters, the second-order ICVNNs can be converted into the form of first-order real-valued differential equations. Then some new flexible and adjustable algebraic criteria to ensure the fixed-time synchronization of ICVNNs are established by means of the non-smooth Lyapunov function and inequality analytical techniques. Moreover, the settling time of fixed-time synchronization is theoretically estimated, which does not depend on the initial value of systems. Finally, simulation examples and applications are presented to illustrate the validity and availability of the obtained results.

Keywords: Fixed-time synchronization; Inertial complex-valued neural networks; Non-smooth Lyapunov function; Inequality analytical techniques (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:405:y:2021:i:c:s0096300321003611

DOI: 10.1016/j.amc.2021.126272

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