Sentence examples for minimize the root from inspiring English sources

These parameters minimize the root mean squared error of the model.

The better option is to, using all measured data, minimize the root mean square (RMS) error with respect to (19).

Actually, training a linear network means finding the optimal setting for the weight matrix W to minimize the root mean squared error (RMSE) of calibration set.

Parameters were selected to minimize the root mean square error (RMSE) between the output of the RNN and the target gene cluster's expression pattern.

For mouse experiments, the value of p was determined by iteratively regressing N and p values against the experimentally determined asymptotic value of M0t to minimize the root mean square value across all tissues using MIDA.

Figures 12 and 13 show differences in activation times between the 3D-3D and 3D-2D models for the rabbit and canine systems, respectively; in the plots, activation times are offset to minimize the root mean square difference.

It's a good idea to dig the hole first, to minimize the roots' exposure to air and light.

The amplitude and phase of the seasonal components (i.e., the values of, P1 and P2) are determined for each YEAR so as to minimize the root-mean-square error of the observed values.

In this paper, a double-loop iterative method has been proposed to minimize the root-mean-squared group-delay error in LS and minimax senses for the design of allpass VFD digital filters.

This method distributes unit masses simulating revolute or prismatic joints depending on the number of assigned degrees of freedom, analyzes the kinetics of unit masses coupled with constraint forces, and designs the existence of these constraint forces to minimize the root-mean-square error of the output paths of synthesized linkages and a target linkage using a genetic algorithm.

The proposed method finds a set of product design variables to minimize the root-mean-squared error (RMSE) of partial least-squares regression (PLSR) models that are used as alternatives when the number of variables is too large to build multiple linear regression models.