Sentence examples for square polynomial from inspiring English sources

The DE optimization method determines a suboptimal solution throughout the optimization on the basis of a spectrally square polynomial and Šiljak's absolute stability test.

The results of least square polynomial regression analysis also illustrate that the mean NST (F=4.95, P=0.30, R2=0.91) and the mean sleep efficiency (F=13.50, P=0.19, R2=0.96) remained constant over the 6-month follow up period.

In a (Delta F) vicinity of (F=F_{rm max}) where (Pi _w) reaches a maximum, (Pi _w(F)) is approximated by a square polynomial (Pi _w=-a_2 F-F_{max })^2+Pi _w=-a_2 F-F_{max

Intensity based normalization methods such as locally weighted least square polynomial regression (lowess) is commonly used in cDNA microarray experiments.

A moving-window least-square polynomial fit (MLPF) method is proposed to smooth MC simulated data.

In the authors' opinion, the new method can eliminate the parasitic impact of the natural environment in the context of the applied inverses of non-square polynomial matrices.

The approach proposed herein is based on the so-called Smith form of non-square polynomial matrices, which is the foundation of the polynomial S-inverse [22, 23].

The designed sliding mode mean-module filter generates the mean-module estimate, which yields a better value of the mean-module criterion in comparison to the conventional mean-square polynomial filter.

Due to the non-square form of (mathbf {underline {{C}}}_{N_{mathrm {R}}times N_{mathrm {T}}}left ({z^{-1}}right)), the authors suggest using new inverses of non-square polynomial matrices [22 25] in the signal reconstruction process [22].

It is shown that the designed sliding mode mean-square filter generates the mean-square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-square polynomial filter Basin et al. (2008) [8], although the gain matrices of both filters are different.

It is demonstrated that the estimates produced by the designed sliding mode mean-square filter and the conventional mean-square polynomial filter yield the same estimation error variance, and there is an advantage in favor of the designed sliding mode mean-module filter.