Robust Stability in Cohen---Grossberg Neural Network with both Time-Varying and Distributed Delays

Authors:
Qian-Kun Song;Jin-De Cao
Affiliations:
Department of Mathematics, Chongqing Jiaotong University, Chongqing, China 400074;Department of Mathematics, Southeast University, Nanjing, China 210096
Venue:
Neural Processing Letters
Year:
2008

Citing 12
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Abstract

In this article, the global exponential robust stability is investigated for Cohen---Grossberg neural network with both time-varying and distributed delays. The parameter uncertainties are assumed to be time-invariant and bounded, and belong to given compact sets. Applying the idea of vector Lyapunov function, M-matrix theory and analysis techniques, several sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential robust stability of the equilibrium point for the neural network. The methodology developed in this article is shown to be simple and effective for the exponential robust stability analysis of neural networks with time-varying delays and distributed delays. The results obtained in this article extend and improve a few recently known results and remove some restrictions on the neural networks. Three examples are given to show the usefulness of the obtained results that are less restrictive than recently known criteria.