Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay

Authors:
Huaguang Zhang;Zhenwei Liu;Guang-Bin Huang;Zhanshan Wang
Affiliations:
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore;School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, China
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

In this paper, a weighting-delay-based method is developed for the study of the stability problem of a class of recurrent neural networks (RNNs) with time-varying delay. Different from previous results, the delay interval [0,d(t)] is divided into some variable subintervals by employing weighting delays. Thus, new delay-dependent stability criteria for RNNs with time-varying delay are derived by applying this weighting-delay method, which are less conservative than previous results. The proposed stability criteria depend on the positions of weighting delays in the interval [0,d(t)], which can be denoted by the weighting-delay parameters. Different weighting-delay parameters lead to different stability margins for a given system. Thus, a solution based on optimization methods is further given to calculate the optimal weighting-delay parameters. Several examples are provided to verify the effectiveness of the proposed criteria.