The phrase "estimation performances" is grammatically correct and can be used in written English, specifically in the context of data analysis and measurement.
It refers to the accuracy and quality of estimating or predicting something, such as a measurement or outcome. Example: "The estimation performances of our new algorithm were impressive, accurately predicting outcomes with an average margin of error of only 3%."
The other modification is a design procedure of the additional output that is proposed in order to improve the disturbance estimation performances.
However, due to unknown disturbances and batch-to-batch variations, the kinetic parameters obtained from experimental study may not represent the real process resulting in poor control and estimation performances.
By means of Monte Carlo simulations conducted over both self-similar and multiplicative cascade synthetic images, it is shown here to benefit from much better practical estimation performances than those obtained from a 2D discrete wavelet transform coefficient analysis.
Estimation performances are discussed for different structures of neural network by taking into account the number and quality of input features, learning algorithms, number of hidden neurons, correlation between network outputs and targets, and statistical error analysis methods.
It is shown that the optimal estimation performances critically depend on the channel accessing probabilities of the network nodes and the packet loss probability, and the optimal filters can be obtained by solving recursive Lyapunov and Riccati equations.
Section 5 analyzes our estimation performances.
Furthermore, a lower bound is given to estimate the ultimate estimation performance before implementing any estimation.
10.7554/eLife.09123.010 Figure 4. Several biologically motivated generalizations of the motion estimator further improved estimation performance without sacrificing glider responses.
Numerical experiments were performed to compare estimation performance of the ML approach with that of MED.
We performed numerical experiments to investigate RCS estimation performance of the ML approach.
Quantitative analysis of the estimation performance and stability is conducted.
