Robust exponential stability criterion for uncertain neural networks with discontinuous activation functions and time-varying delays

Authors:
Xiru Wu;Yaonan Wang;Lihong Huang;Yi Zuo
Affiliations:
College of Electric and Information Technology, Hunan University, Changsha, Hunan 410082, PR China;College of Electric and Information Technology, Hunan University, Changsha, Hunan 410082, PR China;College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, PR China;School of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, PR China and The ARIS Lab, School of Engineering, University of Guelph, Guelph, Ontar ...
Venue:
Neurocomputing
Year:
2010

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Abstract

This paper considers the global robust exponential stability of time-varying delayed neural networks with discontinuous activation functions and norm-bounded uncertainties. Based on the Lyapunov-Krasovskii stability theory, we originally analyze the global robust exponential stability of discontinuous neural networks with time-varying delays in view of the linear matrix inequalities (LMIs). Therefore, our results are brand new compared to previous literatures. A numerical example is given to validate the effectiveness of our results.