Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays

2018 ◽  
Vol 336 ◽  
pp. 338-350 ◽  
Author(s):  
Shiyu Jiao ◽  
Hao Shen ◽  
Yunliang Wei ◽  
Xia Huang ◽  
Zhen Wang
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Time Varying ◽  
Markov Jump ◽  
Generalized Neural Networks

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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