Robust Stability Criterion for Delayed Neural Networks with Discontinuous Activation Functions

Authors:
Yi Zuo;Yaonan Wang;Lihong Huang;Zengyun Wang;Xinzhi Liu;Xiru Wu
Affiliations:
College of Electric and Information Technology, Hunan University, Changsha, P. R. China 410082 and Department of Applied Mathematics, University of Waterloo, Waterloo, Canada N2L 3G1;College of Electric and Information Technology, Hunan University, Changsha, P. R. China 410082;College of Mathematics and Econometrics, Hunan University, Changsha, P. R. China 410082;College of Mathematics and Econometrics, Hunan University, Changsha, P. R. China 410082;Department of Applied Mathematics, University of Waterloo, Waterloo, Canada N2L 3G1;College of Electric and Information Technology, Hunan University, Changsha, P. R. China 410082
Venue:
Neural Processing Letters
Year:
2009

Citing 6
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Abstract

The problem of global robust stability for a class of uncertain delayed neural networks with discontinuous activation functions has been discussed. The uncertainty is assumed to be of norm-bounded form. Based on Lyapunov---Krasovskii stability theory as well as Filippov theory, the conditions are expressed in terms of linear matrix inequality, which make them computationally efficient and flexible. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results.