Finite-Time Stability Analysis for Markovian Jump Memristive Neural Networks With Partly Unknown Transition Probabilities

2017 ◽  
Vol 28 (12) ◽  
pp. 2924-2935 ◽  
Author(s):  
Ruoxia Li ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Finite Time ◽  
Transition Probabilities ◽  
Memristive Neural Networks ◽  
Time Stability ◽  
Markovian Jump ◽  
Finite Time Stability ◽  
Partly Unknown Transition Probabilities

Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method

2017 ◽  
Vol 238 ◽  
pp. 67-75 ◽  
Author(s):  
Mingwen Zheng ◽  
Lixiang Li ◽  
Haipeng Peng ◽  
Jinghua Xiao ◽  
Yixian Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Finite Time ◽  
Lyapunov Method ◽  
Neutral Type ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability

Finite-time stability analysis of fractional-order neural networks with delay

2015 ◽  
Vol 152 ◽  
pp. 19-26 ◽  
Author(s):  
Xujun Yang ◽  
Qiankun Song ◽  
Yurong Liu ◽  
Zhenjiang Zhao
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Fractional Order ◽  
Finite Time ◽  
Time Stability ◽  
Finite Time Stability

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.

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