Open AccessDelay-range-dependent Stability Criteria of Neural Networks with Time-varying Discrete and Distributed Delays
Author(s) -
Kai Hu,
Aiguo Song,
Yingchao Zhang,
WeiLiang Wang
Publication year - 2014
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/53817
Subject(s) - differentiable function , bounded function , computer science , upper and lower bounds , exponential stability , discrete time and continuous time , control theory (sociology) , stability (learning theory) , monotonic function , artificial neural network , range (aeronautics) , mathematics , mathematical optimization , control (management) , nonlinear system , artificial intelligence , mathematical analysis , statistics , physics , materials science , quantum mechanics , machine learning , composite material
This article deals with the global asymptotic stability problem for a class of neural networks with time-varying discrete and distributed delays. The activation functions are assumed to be neither monotonic nor differentiable, and two types of time-varying discrete delays are considered: one is differentiable and has bounded derivatives, and the other is continuous and may vary very fast. By constructing an appropriate Lyapunov-Krasovskii functional and employing a tighter inequality, new stability criteria dependent on both the lower bound and upper bound of the time-varying time delays are established to guarantee asymptotic stability for the addressed neural networks. It is shown that the new criteria improve some results from previous studies. Two simulation examples are given to show the effectiveness and the reduced conservatism of the proposed criteria