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References in periodicals archive ?
Mean square error, E[[[absolute value of [e.sup.(k).sub.i]].sup.2]], can be computed as
By definition the coefficient of the Fourier expansion minimizes the mean square error, so running backpropagation, which minimizes MSE, is an ideal way to find the coefficient.
Spectral region 1727-1690 [cm.sup.-1] Factors 4 Validation standards 3 RMSEC 0.449 RMSECV 2.265 RMSEP 0.324 [R.sup.2] 0.9993 [R.sup.2] is correlation coefficient of actual and calculated values of free fatty acids concentration in the calibration set; RMSEC: root mean square error of calibration; RMSECV: root mean square error of cross-validation; RMSEP: root mean square error of prediction.
The performance of the designed system in terms of mean square error versus number of neurons has been analyzed.
The Hessian matrix is approximated by the square of Jacobian matrix--J (it is true if the performance function is mean square error):
3) Variability of normalized room mean square error, Cv (RMSE), is around 21% which shows measured and simulated data have small variability.
[e.sub.RMSE] stands respectively for the root mean square error of the power forecast of a wind farm.
Applying the mean square error criterion, the best prediction made is the one based on an autoregressive model of order 1 (AR(1)).
3D reconstruction results of non-probe instrument and the mean square error are shown in Table 2 and Fig.
of comparisons 133 6 133 Mean Observed 25.0 0.4 1.2 Calculated 40.7 0.7 4.1 Classical validation parameters Maximum error 55.2 0.8 15.4 Root mean square error 73.0 104.7 325.0 Standardised difference (%) Mean -26.7 -43.9 -55.6 Standard deviation 0.056 0.078 0.089 Standard errorto.os 1.0 8.2 1.5 [t.sub.calc] 54.6 13.8 72.1 P-value <0.003 <0.003 <0.003 [Mg.sub.SE] [Ca.sub.SE] [Cl.sub.SE] No.
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).