Global Asymptotic Stability for a Class of Generalized Neural Networks With Interval Time-Varying Delays

2011 ◽  
Vol 22 (8) ◽  
pp. 1180-1192 ◽  
Author(s):  
Xian-Ming Zhang ◽  
Qing-Long Han
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Time Varying ◽  
Interval Time ◽  
Generalized Neural Networks

Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays

2017 ◽  
Vol 28 (8) ◽  
pp. 1840-1850 ◽  
Author(s):  
Ramasamy Saravanakumar ◽  
Muhammed Syed Ali ◽  
Choon Ki Ahn ◽  
Hamid Reza Karimi ◽  
Peng Shi
Keyword(s):  
Neural Networks ◽  
Time Varying ◽  
Markovian Jump ◽  
Interval Time ◽  
Generalized Neural Networks

scholarly journals New Stability Criterion for Takagi-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Probabilistic Time-Varying Delays

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xiongrui Wang ◽  
Ruofeng Rao ◽  
Shouming Zhong
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Stability Criterion ◽  
Global Asymptotic Stability ◽  
Positive Definite ◽  
Time Varying ◽  
Numerical Example ◽  
Definite Form ◽  
Takagi Sugeno

A new global asymptotic stability criterion of Takagi-Sugeno fuzzy Cohen-Grossberg neural networks with probabilistic time-varying delays was derived, in which the diffusion item can play its role. Owing to deleting the boundedness conditions on amplification functions, the main result is a novelty to some extent. Besides, there is another novelty in methods, for Lyapunov-Krasovskii functional is the positive definite form of p powers, which is different from those of existing literature. Moreover, a numerical example illustrates the effectiveness of the proposed methods.

scholarly journals On Stability Analysis for Generalized Neural Networks with Time-Varying Delays

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
M. J. Park ◽  
O. M. Kwon ◽  
E. J. Cha
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Asymptotic Stability ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Stability Criteria ◽  
Numerical Examples ◽  
Generalized Neural Networks

This paper deals with the problem of stability analysis for generalized neural networks with time-varying delays. With a suitable Lyapunov-Krasovskii functional (LKF) and Wirtinger-based integral inequality, sufficient conditions for guaranteeing the asymptotic stability of the concerned networks are derived in terms of linear matrix inequalities (LMIs). By applying the proposed methods to two numerical examples which have been utilized in many works for checking the conservatism of stability criteria, it is shown that the obtained results are significantly improved comparing with the previous ones published in other literature.

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