Global exponential stability of a class of memristor-based recurrent neural networks with time-varying delays

Authors:
Guodong Zhang;Yi Shen;Junwei Sun
Affiliations:
Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and Key Laboratory of Image Processing and Intelligent Control of Education Minist ...;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and Key Laboratory of Image Processing and Intelligent Control of Education Minist ...;Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China and Key Laboratory of Image Processing and Intelligent Control of Education Minist ...
Venue:
Neurocomputing
Year:
2012

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Abstract

The paper analyzes a general memristor-based recurrent neural networks with time-varying delays (DRNNs). The dynamic analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov, and some new conditions concerning global exponential stability are obtained. In addition, these conditions do not require the activation functions to be differentiable, the connection weight matrices to be symmetric and the delay functions to be differentiable, our results are mild and more general. Finally, numerical simulations illustrate the effectiveness of our results.