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Comparing the results of with that of for each figure, we can also reconfirm that when increases, the estimation error increases on the estimated noise covariance matrix, so we obtain better results with the preprocessing for than for.
When SNR increases, the estimation performance of azimuth and elevation gets better.
Analytical modeling reveals that the lever's compliance significantly increases the estimation error.
It is clear that as the data size increases, the estimation accuracy improves.
Comparing these results, we can remark that when increases the estimation error increases.
Even though method 5 has been designed to deal with harmonic conditions, this design increases the estimation error.
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For instance, Yu et al. (2009) modeled air pollutant concentrations in North and South Carolina (USA) and found that the integration of soft information by the BME method effectively increased the estimation accuracy for O3 predictions compared with estimates derived using BME kriging.
Most importantly, as the sample size increased, the estimation error index TV and the accuracy of estimated total copy numbers or unphased genotypes were improved in the current algorithm, but not always in the previous procedures.
In order to increase the estimation accuracy of the models we propose the SRGM based on Feed-Forward Neural Network (FFNN) approach.
Results show that the MSAS can increase the estimation accuracy and decrease the controller dimensionality and the number of required sensors.
Details on the practical implementation of the control scheme as well as certain options increasing the estimation algorithm's robustness are provided from experimental results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com