Exponential stability of a class of quaternion‐valued memristor‐based neural network with time‐varying delay via M‐matrix
Shengye Wang, Yanchao Shi, Jun Guo · Mathematical Methods in the Applied Sciences · 2024
This paper investigates the problems of exponential stability for a class of quaternion‐valued memristor‐based neural networks. By using M‐matrix theory and fixed point theorem, the existence and uniqueness of the equilibrium point of quaternion‐valued neural network are proved, respectively. Then, by combining M‐matrix with exponential stability theory, a non‐factorization method is obtained by using some inequality techniques to give the effective conditions of global exponential stability of quaternion‐valued memristor‐based neural network with time‐varying delay. Finally, numerical examples are given to demonstrate the validity of the derived results.