Universal analysis method for stability of recurrent neural networks with different multiple delays

Authors:
Zhanshan Wang;Enlin Zhang;Kuo Yun;Huaguang Zhang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, People's Republic of China
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
Year:
2011

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Abstract

A universal stability analysis method on the basis of linear matrix inequality is proposed to solve the stability problem of recurrent neural networks with different kinds of multiple delays. Firstly, a universal neural networks model is analyzed to present a general framework for the stability study, in which a sufficient condition is derived. Secondly, by considering several special case of the universal model, a series of stability criteria are established, which have the same or similar structure and expression. All the obtained stability criteria present a general mode to study the stability of delayed dynamical systems.