Robust stability of Cohen-Grossberg neural networks via state transmission matrix

Authors:
Zhanshan Wang;Huaguang Zhang;Wen Yu
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China;Department de Control Automatico, Mexico City, Mexico
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

This brief is concerned with the global robust exponential stability of a class of interval Cohen-Grossberg neural networks with both multiple time-varying delays and continuously distributed delays. Some new sufficient robust stability conditions are established in the form of state transmission matrix, which are different from the existing ones. Furthermore, a sufficient condition is also established to guarantee the global stability for this class of Cohen-Grossberg neural networks without uncertainties. Three examples are used to show the effectiveness of the obtained results.