Passivity analysis of neural networks with time-varying delays
IEEE Transactions on Circuits and Systems II: Express Briefs
Robust stability analysis for stochastic neural networks with time-varying delay
IEEE Transactions on Neural Networks
A new method for complete stability analysis of cellular neural networks with time delay
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Delay-derivative-dependent stability for delayed neural networks with unbound distributed delay
IEEE Transactions on Neural Networks
Design of QoS in Intelligent Communication Environments Based on Neural Network
Wireless Personal Communications: An International Journal
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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.