Adaptive Synchronization of Fractional-Order Uncertain Complex-Valued Competitive Neural Networks under the Non-Decomposition Method

This paper is devoted to the study of adaptive synchronization for fractional-order uncertain complex-valued competitive neural networks (FOUCVCNNs) using the non-decomposition method. Firstly, a new network model named FOUCVCNNs is proposed, which is not separated into two real-valued subsystems in...

Full description

Saved in:
Bibliographic Details
Published in:Fractal and fractional 2024-08, Vol.8 (8), p.449
Main Authors: Chen, Shenglong, Luo, Xupeng, Yang, Jikai, Li, Zhiming, Li, Hongli
Format: Article
Language:English
Subjects:
Adaptive control
adaptive synchronization
Artificial intelligence
Calculus
Competition
Cost analysis
Decomposition
fractional-order derivative
Liapunov functions
Methods
Neural networks
non-decomposition method
Numerical analysis
Research methodology
Simulation methods
Subsystems
Synchronism
uncertain complex-valued competitive neural networks
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper is devoted to the study of adaptive synchronization for fractional-order uncertain complex-valued competitive neural networks (FOUCVCNNs) using the non-decomposition method. Firstly, a new network model named FOUCVCNNs is proposed, which is not separated into two real-valued subsystems in order to keep its intrinsic speciality. In addition, a novel adaptive controller is designed to reduce the cost of control. Meanwhile, with the help of fractional Lyapunov theory, 1-norm analysis framework and inequality techniques, several effective synchronization criteria for FOUCVCNNs are obtained by constructing an appropriate Lyapunov function. Finally, the reliability of the results obtained is tested through numerical simulation.
ISSN:2504-3110
2504-3110
DOI:10.3390/fractalfract8080449