Delay-Dependent Global Asymptotic Stability in Neutral-Type Delayed Neural Networks with Reaction-Diffusion Terms

2008 ◽  
pp. 158-167 ◽  
Author(s):  
Jianlong Qiu ◽  
Yinlai Jin ◽  
Qingyu Zheng
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Neutral Type ◽  
Reaction Diffusion ◽  
Delayed Neural Networks ◽  
Delay Dependent

scholarly journals Stability and Dissipativity Analysis for Neutral Type Stochastic Markovian Jump Static Neural Networks with Time Delays

2019 ◽  
Vol 9 (3) ◽  
pp. 189-204 ◽  
Author(s):  
Yang Cao ◽  
R. Samidurai ◽  
R. Sriraman
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Global Asymptotic Stability ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Markovian Jump ◽  
Static Neural Networks ◽  
Feasible Solutions ◽  
Delay Dependent

Abstract This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.

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