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Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay

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4 Author(s)
Huaguang Zhang ; Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China ; Zhenwei Liu ; Guang-Bin Huang ; Zhanshan Wang

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 1 )

Date of Publication:

Jan. 2010

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