A stabilization algorithm for a class of uncertain linear systems
Systems & Control Letters
Parametric quadratic stabilizability of uncertain nonlinear systems
Systems & Control Letters
Global exponential stability of recurrent neural networks with time-varying delay
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Adaptive feedback linearization control of chaotic systems via recurrent high-order neural networks
Information Sciences: an International Journal
Stability analysis of Hopfield neural networks with uncertainty
Mathematical and Computer Modelling: An International Journal
Multiperiodicity of Discrete-Time Delayed Neural Networks Evoked by Periodic External Inputs
IEEE Transactions on Neural Networks
International Journal of Advanced Media and Communication
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In this paper, the stability of high-order Hopfield type neural networks with uncertainty is analyzed, the parametric uncertainty is assumed to be bounded. The equilibrium point position may exist for any particular unknown parameter vector in the parameter space, every time one or more of the uncertainty parameters is changed, the equilibrium may shift to a new position or altogether disappear. In the framework of parametric stability, some sufficient conditions are established to guarantee the existence of a globally asymptotically stable equilibrium point for all admissible parametric uncertainties, and the region about the equilibrium point of the nominal part of the neural network that contains the equilibria for each parameter vector in the given subset of the parameter space be estimated.