New delay-independent exponential stability rule of delayed Cohen-Grossberg neural networks. (2nd June 2023)

• Record Type: Journal Article
• Authors: Zheng, Cheng-De; Meng, Haorui; Liu, Shengzhou
• Is Part Of: International journal of innovative computing and applications. Volume 14:Number 3(2023)
• Page Start: 125
• Page End: 131
• Publication Date: 2023-06-02
• Subjects: stability -- inequality -- delay -- homeomorphism; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

This manuscript studies the stability for a class of Cohen-Grossberg neural networks (CGNNs) with variable delays. By practicing the scheme of Lyapunov function (LF), M-matrix (MM) theory, homeomorphism theory and nonlinear measure (NM) method, a new sufficient condition is obtained to ensure the existence, uniqueness and global exponential stability (GES) of equilibrium point (EP) for the studied network. As the condition is independent to delay, it can be applied to networks with large delays. The result generalises and improves the earlier publications. Finally, an example is supplied to exhibit the power of the results and less conservativeness over some earlier publications.