Novel fixed-time synchronization results of fractional-order fuzzy cellular neural networks with delays and interactions-Reference-Cited by-同舟云学术

Novel fixed-time synchronization results of fractional-order fuzzy cellular neural networks with delays and interactions

Published:2024 Issue:5 Volume:9 Page:13245-13264
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Liu Jun¹²,Deng Wenjing¹,Sun Shuqin³,Shi Kaibo⁴

Affiliation:

1. School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu Sichuan 611731, China

2. Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, Sichuan 610068, China

3. School of Mathematics Education, China West Normal University, Nanchong Sichuan 637002, China

4. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu, Sichuan 610106, China

Abstract

<abstract><p>This research investigated the fixed-time (FXT) synchronization of fractional-order fuzzy cellular neural networks (FCNNs) with delays and interactions based on an enhanced FXT stability theorem. By conceiving proper Lyapunov functions and applying inequality techniques, several sufficient conditions were obtained to vouch for the fixed-time synchronization (FXTS) of the discussed systems through two categories of control schemes. Moreover, in terms of another FXT stability theorem, different upper-bounding estimating formulas for settling time (ST) were given, and the distinctions between them were pointed out. Two examples were delivered at length to demonstrate the conclusions.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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