LMI approach for global robust stability of Cohen-Grossberg neural networks with multiple delays

Authors:
C. Ji;H. G. Zhang;Y. Wei
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, People's Republic of China
Venue:
Neurocomputing
Year:
2008

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Abstract

In this paper, we investigate the global robust stability of the equilibrium point of a class of Cohen-Grossberg neural networks with multiple delays and uncertainties. The new criteria for the global robust stability are given by way of constructing a suitable Lyapunov functional. The criteria take the form of linear matrix inequality (LMI), and are independent of the amplification function. Compared with the other robust stability results, they turn out to be less restrictive. In addition, all results are established without assuming any symmetry of the interconnecting matrix, and the differentiability and monotonicity of activation functions. A simulation example is also given to illustrate the effectiveness of our results.