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Measurement error can be minimized using rigorous B-spline data alignment.
On combining PI control and sliding mode control the steady error can be minimized.
This chapter covers common photovoltaic measurement techniques, and the ways in which problems and sources of error can be minimized.
Here an interaction in the construction of prototypes is optimized so that the bidirectional recall error can be minimized.
In other words, if the selected codewords are used to train the regression model, the expected prediction error can be minimized.
The basic idea of PCA is to project the data along the directions of maximal variances so that the reconstruction error can be minimized.
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However, these errors can be minimized by harnessing the information obtained from the initial reliabilities.
Sampling errors can be minimized with the use of fine solids and sampling at as high a velocity as possible.
However, the classification errors can be minimized if we minimize the overlap between the genuine and the impostor score distributions.
Special attention is given to the implications of present hindcast errors and how the hindcast errors can be minimized.
It is further shown that such errors can be minimized by use of multipole expansion techniques similar to those used in acoustic radiation problems.
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