Synchronization of discrete-time fractional fuzzy neural networks with delays via quantized control

Jikai Yang; Hong-Li Li; Long Zhang; Cheng Hu; Haijun Jiang

doi:10.1016/j.isatra.2023.06.037

Synchronization of discrete-time fractional fuzzy neural networks with delays via quantized control

ISA Trans. 2023 Oct:141:241-250. doi: 10.1016/j.isatra.2023.06.037. Epub 2023 Jul 4.

Authors

Jikai Yang¹, Hong-Li Li², Long Zhang¹, Cheng Hu¹, Haijun Jiang¹

Affiliations

¹ College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
² College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China; School of Mathematics, Southeast University, Nanjing 210096, China. Electronic address: lihongli@xju.edu.cn.

PMID: 37451923
DOI: 10.1016/j.isatra.2023.06.037

Abstract

In this paper, synchronization issue of discrete-time fractional fuzzy neural networks (DFFNNs) with delays is solved via quantized control, and is applied in image encryption. Firstly, a novel fractional-order h-difference inequality which makes Lyapunov method more flexible and practical is strictly proved based on the properties of convex functions and theory of discrete fractional calculus. Secondly, by using compression mapping theorem and mathematical induction, we obtain two sufficient conditions to ensure the existence and uniqueness of solutions for DFFNNs. Whereafter, we design a suitable quantized controller, which not only saves channel resources but also reduces control costs. By utilizing our inequality and some analytical techniques, several conservative synchronization criteria for DFFNNs are acquired. Finally, two examples are arranged to illustrate the validity and practicability of our results.

Keywords: Compression mapping theorem; Fuzzy neural networks; Quantized control; Synchronization; h-difference.