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

Highlights：

• Compared with the works [3,13,36-38,42], what we get herein is fixed-time synchronization, and these results are also applied to image encryption.

• Different from the real-valued INNs [5–7,19,21] and first-order CVNNs [13,39-42]. In this paper, the neural model is defined in the complex field and has an inertial term, which broadens the application range of INNs and makes up for some previous literature results.

摘要

•In this paper, the p-norm fixed-time synchronization of delayed ICVNNs is investigated for the first time, in which the settling time estimation in terms of p-norm is derived. Our results are more flexible and adjustable than these results [36–38].•Compared with the works [3,13,36-38,42], what we get herein is fixed-time synchronization, and these results are also applied to image encryption.•Different from the real-valued INNs [5–7,19,21] and first-order CVNNs [13,39-42]. In this paper, the neural model is defined in the complex field and has an inertial term, which broadens the application range of INNs and makes up for some previous literature results.

论文关键词：Fixed-time synchronization,Inertial complex-valued neural networks,Non-smooth Lyapunov function,Inequality analytical techniques

论文评审过程：Received 18 October 2020, Revised 3 March 2021, Accepted 4 April 2021, Available online 22 April 2021, Version of Record 22 April 2021.