This alleviates our estimates from the well-known dynamic panel data estimation problems, and allows us to proceed with System OLS, rather than System GMM, estimation.13.
To this end, the sampled-data estimation problem has been solved and the desired estimator can be designed by Theorem 1.
In this paper, we deal with a general missing-data spectral estimation problem for which we develop two nonparametric missing-data amplitude and phase estimation (MAPES) algorithms, both of which make use of the expectation maximization (EM) algorithm.
This paper investigates the sampled-data state estimation problem for a class of delayed complex networks.
In this paper, the sampled-data state estimation problem will be investigated.
This study examines the sampled-data state estimation problem for genetic regulatory networks (GRNs) with time-varying delays.
Motivated by the above discussion, in this paper, we aim to deal with the sampled-data state estimation problem for neural networks of neutral type.
Finally, we compare our proposal with a recent representative TM estimation algorithm through both real experiments and extensive simulations, the results show that, (i) the data center TM estimation problem could be well handled after the decomposition step, (ii) our two algorithms outperforms the former one in both speed and accuracy.
Given a model of a nonlinear dynamic system and a set of experimental data, the parameter estimation problem consists of finding the optimal vector of decision variables p (unknown model parameters).
Given a model of a nonlinear dynamic system and a set of experimental data, the parameter estimation problem consists of finding the vector of decision variables p unknown model parameters) that minimizes a cost function that measures the goodness of the fit of the model predictions with respect to the data, subject to a number of constraints.
This paper deals with the distributed sampled-data H∞ state estimation problem for a class of continuous-time nonlinear systems with infinite-distributed delays.
