Improved delay-dependent asymptotic stability criteria for delayed neural networks.
This brief is concerned with asymptotic stability of neural networks with uncertain delays. Two types of uncertain delays are considered: one is constant while the other is time varying. The discretized Lyapunov-Krasovskii functional (LKF) method is integrated with the technique of introducing the free-weighting matrix between the terms of the Leibniz-Newton formula. The integrated method leads to the establishment of new delay-dependent sufficient conditions in form of linear matrix inequalities for asymptotic stability of delayed neural networks (DNNs). A numerical simulation study is conducted to demonstrate the obtained theoretical results, which shows their less conservatism than the existing stability criteria.
Chen
Wu-Hua
WH
College of Mathematics and Information Science, Guangxi University, Nanning, Guangxi 530004, China.
Zheng
Wei Xing
WX
D000465:Algorithms; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013997:Time Factors