Sentence examples for order estimation algorithm from inspiring English sources
The objective of this paper is to develop an order estimation algorithm for model identification of ill-conditioned processes using subspace methods.
The first-order estimation algorithm with interaction (FOCE-I) was used.
All models were fitted to data using the first-order conditional estimation algorithm, except the regression model in (3) which was fitted using the first-order algorithm.
Indubitably and expectedly, our second order approximation algorithm provides an even further improvement in frequency tracking and channel estimation performance over the first order counterpart.
In order to derive the estimation algorithm, local estimates are combined to form the global estimate.
However, in order to simplify the estimation algorithm, only the stiffness parameters are estimated, with mass fixed as a constant.
In order to employ Newton Raphson estimation algorithm as an iteration method in custom-built discrete choice modeling, it is needed to derive the second-order partial derivatives of the log-likelihood formulation for the given choice situation.
Then, the gathered backscattered echoes are estimated using a model-based estimation algorithm in order to extract the desired elastic properties.
Simulation results show that both the above first order and second order approximation algorithms have faster convergence rate as well as better estimation accuracies than other existing algorithms including the gradient algorithm.
In order to evaluate the respiration rate estimation algorithm, we used the simulated ECG data with varying respiration rates and under different heart rates (60 100 beats/minute) and signal-to-noise ratio scenarios (10, 20, and 40 dB).
In this paper, the problem of direction-of-arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar with gain-phase errors is addressed, by using a sparse DOA estimation algorithm with fourth-order cumulants (FOC) based error matrix estimation.
