Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays

Complexity ◽  
2014 ◽  
Vol 21 (5) ◽  
pp. 59-72 ◽  
Author(s):  
Chandrasekar Pradeep ◽  
Arunachalam Chandrasekar ◽  
Rangasamy Murugesu ◽  
Rajan Rakkiyappan
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Stability Analysis

New results of robust stability analysis for neutral-type neural networks with time-varying delays and Markovian jumping parameters1The work of authors was supported by Department of Science and Technology, New Delhi, India, under the sanctioned No. SR/S4/MS:485/07.

2011 ◽  
Vol 89 (8) ◽  
pp. 827-840 ◽  
Author(s):  
S. Lakshmanan ◽  
P. Balasubramaniam
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Neutral Type ◽  
Linear Matrix ◽  
New Delhi ◽  
Time Varying ◽  
Markovian Jumping ◽  
Decomposition Approach ◽  
Robust Stability Analysis

In this paper, robust stability analysis for neutral-type neural networks with time-varying delays and Markovian jumping parameters is conducted. By using the delay-decomposition approach, a new Lyapunov–Krasovskii functional is constructed. Based on this Lyapunov–Krasovskii functional and some stochastic stability theory, delay-dependent stability criteria are obtained in terms of linear matrix inequalities. Finally, three numerical examples are given to illustrate the effectiveness and reduced conservatism of our theoretical results.

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