Paper Info
Title
Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays
Abstract
This paper studies the global asymptotic stability problem for a class of stochastic complex-valued neural networks (SCVNNs) with probabilistic time-varying delays as well as stochastic disturbances. Based on the Lyapunov–Krasovskii functional (LKF) method and mathematical analytic techniques, delay-dependent stability criteria are derived by separating complex-valued neural networks (CVNNs) into real and imaginary parts. Furthermore, the obtained sufficient conditions are presented in terms of simplified linear matrix inequalities (LMIs), which can be straightforwardly solved by Matlab. Finally, two simulation examples are provided to show the effectiveness and advantages of the proposed results.
Year
DOI
Venue
2020
10.1016/j.matcom.2019.04.001
Mathematics and Computers in Simulation
Keywords
Field
DocType
Global asymptotic stability,Complex-valued neural networks,Stochastic disturbance,Lyapunov–Krasovskii functional,Probabilistic time-varying delays
Mathematical optimization,MATLAB,Matrix (mathematics),Exponential stability,Probabilistic logic,Artificial neural network,Mathematics
Journal
Volume
ISSN
Citations 
171
0378-4754
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
R. Sriraman1214.67
Yang Cao220224.82
R. Samidurai327515.47