Global asymptotic stability of recurrent neural networks with multiple time-varying delays.
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.
Zhang
Huaguang
H
School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, PR China. hgzhang@ieee.org
Wang
Zhanshan
Z
Liu
Derong
D
D000143:Acids / Q000737:chemistry; D000465:Algorithms; D016014:Linear Models; D016571:Neural Networks, Computer
D000143:Acids