Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

2011 ◽  
Vol 20 (4) ◽  
pp. 040204 ◽  
Author(s):  
S Lakshmanan ◽  
P Balasubramaniam
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Matrix Inequality ◽  
Time Varying Delay ◽  
Robust Stability Analysis ◽  
Linear Matrix Inequality Approach ◽  
Varying Delay

Exponential Stability for Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

2015 ◽  
Vol 742 ◽  
pp. 399-403
Author(s):  
Ya Jun Li ◽  
Jing Zhao Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Leakage Delay ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Time Varying Delay ◽  
Varying Delay

This paper investigates the exponential stability problem for a class of stochastic neural networks with leakage delay. By employing a suitable Lyapunov functional and stochastic stability theory technic, the sufficient conditions which make the stochastic neural networks system exponential mean square stable are proposed and proved. All results are expressed in terms of linear matrix inequalities (LMIs). Example and simulation are presented to show the effectiveness of the proposed method.

Further Results on Exponential Robust Stability Analysis for Recurrent Neural Networks With Time-Varying Delay

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Pin-Lin Liu
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Stability Conditions ◽  
Time Varying ◽  
Robust Exponential Stability ◽  
Time Varying Delay ◽  
Robust Stability Analysis ◽  
The Relationship ◽  
Varying Delay

In this paper, the problems of determining the robust exponential stability and estimating the exponential convergence rate for recurrent neural networks (RNNs) with parametric uncertainties and time-varying delay are studied. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. The developed stability conditions are in terms of linear matrix inequalities (LMIs) and the integral inequality approach (IIA), which can be checked easily by recently developed algorithms solving LMIs. Furthermore, the proposed stability conditions are less conservative than some recently known ones in the literature, and this has been demonstrated via four examples with simulation.

IMPROVED ROBUST STABILITY CRITERION FOR UNCERTAIN CELLULAR NEURAL NETWORKS WITH TIME-VARYING DELAY

2010 ◽  
Vol 24 (04n05) ◽  
pp. 503-511 ◽  
Author(s):  
S. M. LEE
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Stability Criterion ◽  
Nonlinear Function ◽  
Cellular Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Varying Delay ◽  
Robust Stability Analysis ◽  
Varying Delay

In this paper, we propose a new robust stability analysis method for uncertain cellular neural networks with time-varying delay. The proposed stability criterion is based on the Lyapunov function with sector bounded nonlinear function. The sufficient condition for the stability is derived in terms of LMI (linear matrix inequality). Numerical examples show the effectiveness of the proposed method.

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