Delay-Slope-Dependent Stability Results of Recurrent Neural Networks

Authors:
Tao Li;Wei Xing Zheng;Chong Lin
Affiliations:
Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing, China;School of Computing and Mathematics, University of Western Sydney, Penrith, Australia;Institute of Complexity Science, College of Automation Engineering, Qingdao University, Qingdao, China
Venue:
IEEE Transactions on Neural Networks - Part 1
Year:
2011

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Abstract

By using the fact that the neuron activation functions are sector bounded and nondecreasing, this brief presents a new method, named the delay-slope-dependent method, for stability analysis of a class of recurrent neural networks with time-varying delays. This method includes more information on the slope of neuron activation functions and fewer matrix variables in the constructed Lyapunov–Krasovskii functional. Then some improved delay-dependent stability criteria with less computational burden and conservatism are obtained. Numerical examples are given to illustrate the effectiveness and the benefits of the proposed method.