Global exponential stability analysis for recurrent neural networks with time-varying delay

2009 ◽  
Author(s):  
Xiaoli Guo ◽  
Qingbo Li ◽  
Yonggang Chen ◽  
Yuanyuan Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Recurrent Neural Networks ◽  
Global Exponential Stability ◽  
Time Varying ◽  
Time Varying Delay ◽  
Varying Delay

scholarly journals Novel Global Exponential Stability Criterion for Recurrent Neural Networks with Time-Varying Delay

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Wenguang Luo ◽  
Xiuling Wang ◽  
Yonghua Liu ◽  
Hongli Lan
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Stability Criterion ◽  
Recurrent Neural Networks ◽  
Global Exponential Stability ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Delay Dependent ◽  
Varying Delay

The problem of global exponential stability for recurrent neural networks with time-varying delay is investigated. By dividing the time delay interval [0,τ(t)] intoK+1dynamical subintervals, a new Lyapunov-Krasovskii functional is introduced; then, a novel linear-matrix-inequality (LMI-) based delay-dependent exponential stability criterion is derived, which is less conservative than some previous literatures (Zhang et al., 2005; He et al., 2006; and Wu et al., 2008). An illustrate example is finally provided to show the effectiveness and the advantage of the proposed result.

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