Least

mean square Algorithm provides an effective method to compute the optimal solution in terms of least

mean square.

The root

mean square values of the battery pack acceleration prove the phenomenon that is shown in Table 3.

RMSE] stands respectively for the root

mean square error of the power forecast of a wind farm.

A higher order model will produce lower error give the best fit in sample, but when the model is used for out of sample forecasting purpose, it is likely to produce worse forecast than the lower order model, since the

mean square error of the forecasts errors will not affected by only the stationary variance of the model but also by errors arising from the estimation of the parameters of the model (Brockwell, Davis 2002).

On the other hand, comparing with reference data from the archives of datasets of the Estonian Land Board--catalogue of triangulation points in the Pulkovo 1942 datum (archive numbers GF-1-10-II-258-GF-1-10-II-283), we see that the datum transform EPSG:1334 produces a

mean square deviation of approximately 1.

10) and show that the state of every follower will track that of the leader in the sense of

mean square convergence, that is, E[parallel]x(t) - [x.

The

mean square of P x L interaction for GY was not significant in both selection cycles, evidencing that besides location presented edafoclimatic differences, progenies obtained similar performances.

Root

mean square (rms) deviation from the mean value of [T.

Table 1 contains the presently known data of the

mean square charge radius (([rho][r.

From the above tables it is evident that the network 4-90-50-7 provides better prediction with root

mean square error for training data for NOx, smoke and exhaust gas temperature of 4.

The air turbulence intensity, defined with the coefficient between air velocity root

mean square and the air velocity mean value, presented in this study, is calculated using the local mean air velocity value.

The main features that attract [4] the use of the Least

mean square algorithm are its low computational complexity, proof of convergence in stationary environment, unbiased convergence in the mean of the Wiener solution and stable behavior, robust performance against different signal conditions.