On the Weierstrass-Stone theorem
Journal of Approximation Theory
Mathematical Analysis of HIV-1 Dynamics in Vivo
SIAM Review
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks and Artificial Intelligence for Biomedical Engineering
Neural Networks and Artificial Intelligence for Biomedical Engineering
IEEE Transactions on Neural Networks
Two Types of Haar Wavelet Neural Networks for Nonlinear System Identification
Neural Processing Letters
Hi-index | 0.00 |
State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with the state observation problem when the dynamic model of a plant contains uncertainties or is completely unknown and it is oriented to discrete time nonlinear systems because most of the existent results have been developed for continuous time systems. The recurrent neural network (RNN) have shown his advantages to deal with this class of problem. The Lyapunov second method is applied to generate a new learning law, containing an adaptive adjustment rate, implying the stability condition for the free parameters of the neural-observer. A numerical example is given using the RNN in the estimation of a mathematical model of HIV infection with three states.