Stability analysis for Cohen-Grossberg neural networks with time-varying delays
Wu‐Hua Chen, Wei Xing Zheng · 2006
The problems of existence, uniqueness and global exponential stability of the equilibrium of Cohen-Grossberg neural networks with time-varying delays are investigated in this paper. A new approach is developed to establish delay-independent/dependent sufficient conditions for global exponential stability. The results obtained can be easily checked in practice and do not require the delays to be constant or differentiate. In particular, our delay-dependent exponential stability conditions give explicitly the allowable upper bound of the delays that guarantees stability of Cohen-Grossberg neural networks, and are applicable to the case when the non-delayed terms cannot dominate the delayed terms. The effectiveness of the new results are further illustrated by numerical examples in comparison with the existing results.