Quasi-Stabilization Control of Quaternion-Valued Fractional-Order Memristive Neural Networks

This paper focuses on the quasi-stabilization of the quaternion-valued fractional-order memristive neural networks. Based on the contraction mapping theory, a sufficient condition is derived to ensure the existence of the equilibrium point for the memristive neural networks. Subsequently, by means o...

Full description

Saved in:
Bibliographic Details
Published in:Circuits, systems, and signal processing systems, and signal processing, 2022-12, Vol.41 (12), p.6733-6749
Main Authors: Li, Ruoxia, Cao, Jinde
Format: Article
Language:English
Subjects:
Algebra
Circuits and Systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Equilibrium
Instrumentation
Neural networks
Personal computers
Quaternions
Signal processing
Signal,Image and Speech Processing
Stabilization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper focuses on the quasi-stabilization of the quaternion-valued fractional-order memristive neural networks. Based on the contraction mapping theory, a sufficient condition is derived to ensure the existence of the equilibrium point for the memristive neural networks. Subsequently, by means of Lyapunov functional and fractional Laplace transform, a algebraic inequality-based condition is developed to guarantee the quasi-stability of the equilibrium point. In addition, a related question is whether the convex closure proposed by the quaternion parameters is meaningful, to overcome this issues, a vector ordering approach is proposed, which can be used to compare the “magnitude” of two different quaternions. Finally, the corresponding simulation results are included to show the effectiveness of the proposed methodology derived in this paper.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-022-02105-4