Stability analysis of delayed neural networks based on a relaxed delay-product-type Lyapunov functional

2021 ◽  
Vol 439 ◽  
pp. 340-347
Author(s):  
Yun Chen ◽  
Gang Chen
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Lyapunov Functional ◽  
Product Type ◽  
Delayed Neural Networks

Improved Results on Finite-Time Stability Analysis of Neural Networks With Time-Varying Delays

2018 ◽  
Vol 140 (10) ◽  
Author(s):  
S. Saravanan ◽  
M. Syed Ali
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Finite Time ◽  
Lyapunov Functional ◽  
Convex Combination ◽  
Linear Matrix ◽  
Time Stability ◽  
Delayed Neural Networks ◽  
Finite Time Stability ◽  
Combination Methods

This paper investigates the issue of finite time stability analysis of time-delayed neural networks by introducing a new Lyapunov functional which uses the information on the delay sufficiently and an augmented Lyapunov functional which contains some triple integral terms. Some improved delay-dependent stability criteria are derived using Jensen's inequality, reciprocally convex combination methods. Then, the finite-time stability conditions are solved by the linear matrix inequalities (LMIs). Numerical examples are finally presented to verify the effectiveness of the obtained results.

Exponential Stability Analysis for Neutral Stochastic Delayed Neural Networks

2011 ◽  
Vol 354-355 ◽  
pp. 877-880
Author(s):  
Min Gang Hua ◽  
Jun Tao Fei ◽  
Wei Li Dai
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Lyapunov Functional ◽  
Stability Criteria ◽  
Delayed Neural Networks ◽  
The Stability ◽  
Delay Dependent ◽  
Delay Dependent Stability

In this paper, the generalized Finsler lemma and augmented Lyapunov functional are introduced to establish some improved delay-dependent stability criteria of neutral stochastic delayed neural networks. The stability criteria in the new results improve and generalize existing ones. Two examples are included to show the effectiveness of the results.

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