Global attractivity in delayed Hopfield neural network models
SIAM Journal on Applied Mathematics
Delay-dependent robust H∞ control of uncertain linear systems with time-varying delays
Computers & Mathematics with Applications
Technical Communique: Delay-dependent criteria for robust stability of time-varying delay systems
Automatica (Journal of IFAC)
Robust stability for interval Hopfield neural networks with time delay
IEEE Transactions on Neural Networks
How delays affect neural dynamics and learning
IEEE Transactions on Neural Networks
Delay-independent stability in bidirectional associative memory networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Mathematical and Computer Modelling: An International Journal
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This paper presents a new approach to the analysis of asymptotic stability of artificial neural networks (ANN) with multiple time-varying delays subject to polytope-bounded uncertainties. This approach is based on the Lyapunov-Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique with the use of a recent Leibniz-Newton model based transformation without including any additional dynamics. Three examples with numerical simulations are used to illustrate the effectiveness of the proposed method. The first example considers the neural network with multiple time-varying delays, which may be seen as a particular case of the second example where it is subject to uncertainties and multiple time-varying delays. Finally, the third example analyzes the stability of the neural network with higher numbers of neurons subject to a single time-delay. The Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability in the bifurcation point.