scholarly journals Improved stability criteria for generalized neural networks with time-varying delay by auxiliary function-based integral inequality

2016 ◽  
Vol 2016 (1) ◽  
Author(s):  
Zhi-Wen Chen ◽  
Jun Yang ◽  
Wen-Pin Luo
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Auxiliary Function ◽  
Time Varying ◽  
Stability Criteria ◽  
Time Varying Delay ◽  
Generalized Neural Networks ◽  
Improved Stability ◽  
Varying Delay

Improved stability criteria for neural networks with time-varying delay

2009 ◽  
Vol 373 (3) ◽  
pp. 342-348 ◽  
Author(s):  
Jian Sun ◽  
G.P. Liu ◽  
Jie Chen ◽  
D. Rees
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Time Varying Delay ◽  
Improved Stability ◽  
Varying Delay

scholarly journals New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

2009 ◽  
Vol 2009 ◽  
pp. 1-23 ◽  
Author(s):  
Zixin Liu ◽  
Shu Lv ◽  
Shouming Zhong ◽  
Mao Ye
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Stability Criteria ◽  
Connection Weight ◽  
Time Varying Delay ◽  
Improved Stability ◽  
Varying Delay

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method.

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