Globally exponential stability of non-autonomous delayed neural networks

Authors:
Qiang Zhang;Wenbing Liu;Xiaopeng Wei;Jin Xu
Affiliations:
University Key Lab of Information Science & Engineering, Dalian University, Dalian, China;School of Computer Science and Engineering, Wenzhou Normal College, Wenzhou, China;University Key Lab of Information Science & Engineering, Dalian University, Dalian, China;University Key Lab of Information Science & Engineering, Dalian University, Dalian, China
Venue:
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

Globally exponential stability of non-autonomous delayed neural networks is considered in this paper. By utilizing delay differential inequalities, a new sufficient condition ensuring globally exponential stability for non-autonomous delayed neural networks is presented. The condition does not require that the delay function be differentiable or the coefficients be bounded. Due to this reason, the condition improves and extends those given in the previous literature.