LMI-Based Approach for Global Asymptotic Stability Analysis of Discrete-Time Cohen-Grossberg Neural Networks

Authors:
Sida Lin;Meiqin Liu;Yanhui Shi;Jianhai Zhang;Yaoyao Zhang;Gangfeng Yan
Affiliations:
Office of Zhejiang Provincial Natural Science Foundation, Hangzhou 310007, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;Shijiazhuang Railway Institute, Shijiazhuang 050043, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 3
Cited 0

Global exponential stability of discrete-time Cohen-Grossberg neural networks

Neurocomputing
Stability analysis of Cohen-Grossberg neural networks

IEEE Transactions on Neural Networks
Improved conditions for global exponential stability of recurrent neural networks with time-varying delays

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The global asymptotic stability of discrete-time Cohen---Grossberg neural networks (CGNNs) with or without time delays is studied in this paper. The CGNNs are transformed into discrete-time interval systems, and several sufficient conditions of asymptotic stability for these interval systems are derived by constructing some suitable Lyapunov functionals. The obtained conditions are given in the form of linear matrix inequalities that can be checked numerically and very efficiently by resorting to the MATLAB LMI Control Toolbox.