Preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and Markov parameters

Hongliang Liu; Jun Cheng; Jinde Cao; Iyad Katib

doi:10.1016/j.neunet.2023.11.011

Preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and Markov parameters

Neural Netw. 2024 Jan:169:520-531. doi: 10.1016/j.neunet.2023.11.011. Epub 2023 Nov 7.

Authors

Hongliang Liu¹, Jun Cheng², Jinde Cao³, Iyad Katib⁴

Affiliations

¹ School of Mathematics and Physics, University of South China, Hengyang, 421001, PR China.
² School of Mathematics and Statistics, Guangxi Normal University, Guilin, 541004, PR China. Electronic address: jcheng@gxnu.edu.cn.
³ School of Mathematics, Southeast University, Nanjing 210096, PR China.
⁴ Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

PMID: 37948970
DOI: 10.1016/j.neunet.2023.11.011

Abstract

This study addresses the preassigned-time synchronization for complex-valued memristive neural networks with reaction-diffusion terms and Markov parameters. Employing a preassigned-time stable control strategy, two distinct controllers with varying power exponent parameters are designed to ensure that synchronization can be achieved within a predefined time frame. Unlike existing finite/fixed-time results, a priori specification of the settling time is addressed. Furthermore, Green's formula and boundary conditions are efficiently applied to overcome potential symmetry loss. Additionally, the activation function's constraint range is more lenient compared to existing constraints. Finally, the effectiveness of the presented methods are demonstrated through two examples.

Keywords: Complex-valued; Markovian jump parameters; Preassigned-time synchronization; Reaction–diffusion neural networks.

MeSH terms

Diffusion
Neural Networks, Computer*
Time Factors