scholarly journals Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis

2007 ◽  
Vol 362 (5-6) ◽  
pp. 480-488 ◽  
Author(s):  
Yurong Liu ◽  
Zidong Wang ◽  
Alan Serrano ◽  
Xiaohui Liu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Time Varying

scholarly journals Exponential Stability Results of Discrete-Time Stochastic Neural Networks with Time-Varying Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yajun Li
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Discrete Time ◽  
Computational Efficiency ◽  
Model Transformation ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Numerical Examples ◽  
Stability Results

An innovative stability analysis approach for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays is developed. By constructing a novel piecewise Lyapunov-Krasovskii functional candidate, a new sum inequality is presented to deal with sum items without ignoring any useful items, the model transformation is no longer needed, and the free weighting matrices are added to reduce the conservatism in the derivation of our results, so the improvement of computational efficiency can be expected. Numerical examples and simulations are also given to show the effectiveness and less conservatism of the proposed criteria.

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