Title: New delay-independent exponential stability rule of delayed Cohen-Grossberg neural networks

Authors: Cheng-De Zheng; Haorui Meng; Shengzhou Liu

Addresses: School of Science, Dalian Jiaotong University, Dalian, 116028, China ' School of Science, Dalian Jiaotong University, Dalian, 116028, China ' School of Science, Dalian Jiaotong University, Dalian, 116028, China

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.

Keywords: stability; inequality; delay; homeomorphism.

DOI: 10.1504/IJICA.2023.131353

International Journal of Innovative Computing and Applications, 2023 Vol.14 No.3, pp.125 - 131

Received: 05 Oct 2020
Accepted: 26 May 2021
Published online: 07 Jun 2023 *