Global exponential dissipativity of neutral-type BAM inertial neural networks with mixed time-varying delays

2020 ◽  
Vol 378 ◽  
pp. 399-412 ◽  
Author(s):  
Liyan Duan ◽  
Jigui Jian ◽  
Baoxian Wang
Keyword(s):  
Neural Networks ◽  
Neutral Type ◽  
Time Varying ◽  
Inertial Neural Networks

scholarly journals Periodic solution for neutral-type inertial neural networks with time-varying delays

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Mei Xu ◽  
Bo Du
Keyword(s):  
Neural Networks ◽  
Lyapunov Functional ◽  
Neutral Type ◽  
Functional Method ◽  
Time Varying ◽  
Mawhin’S Continuation Theorem ◽  
Existence And Stability ◽  
Inertial Neural Networks ◽  
Lyapunov Functional Method ◽  
Theoretical Results

Abstract In this paper the problems of the existence and stability of periodic solutions of neutral-type inertial neural networks with time-varying delays are discussed by applying Mawhin’s continuation theorem and Lyapunov functional method. Finally, two numerical examples are given to illustrate our theoretical results.

On Robust Stability for Stochastic Neural Networks of Neutral-Type with Uncertainties and Time-Varying Delays

2012 ◽  
Vol 457-458 ◽  
pp. 716-722
Author(s):  
Guo Quan Liu ◽  
Simon X. Yang
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Neutral Type ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Stability Criteria ◽  
Analysis Problem ◽  
Analysis Technique ◽  
Robust Stability Analysis

This paper is concerned with the robust stability analysis problem for stochastic neural networks of neutral-type with uncertainties and time-varying delays. Novel stability criteria are proposed in terms of linear matrix inequality (LMI) by defining a Lyapunov-Krasovskii functional and using the stochastic analysis technique. Two examples are given to show the effectiveness of the obtained conditions.

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