Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay.
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.
Zhang
Huaguang
H
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China. hgzhang@ieee.org
Liu
Zhenwei
Z
Huang
Guang-Bin
GB
Wang
Zhanshan
Z
D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012008:Recurrence; D013997:Time Factors