Title :
Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays
Author :
Zhang, Huaguang ; Wang, Zhanshan ; Liu, Derong
Author_Institution :
Northeastern Univ., Shenyang
fDate :
5/1/2008 12:00:00 AM
Abstract :
In this paper, several sufficient conditions are established for the global asymptotic stability of recurrent neural networks with multiple time-varying delays. The Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) approach are employed in our investigation. The results are shown to be generalizations of some previously published results and are less conservative than existing results. The present results are also applied to recurrent neural networks with constant time delays.
Keywords :
Lyapunov methods; asymptotic stability; delay systems; differential equations; functional equations; linear matrix inequalities; neurocontrollers; recurrent neural nets; time-varying systems; Lyapunov-Krasovskii stability; functional differential equations; global asymptotic stability; linear matrix inequality; multiple time-varying delays; recurrent neural network; Lyapunov–Krasovskii functional; Recurrent neural networks; global asymptotic stability; linear matrix inequality (LMI); multiple time-varying delays; Acids; Algorithms; Linear Models; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2007.912319
