Other classical validation parameters (Loague and Green 1991) such as maximum error and root mean

**square error**are also reported in this study.This definition appears very frequently and is found in the following Acronym Finder categories:

- Science, medicine, engineering, etc.

See other **definitions of SE**

Other Resources:

We have 106 other **meanings of SE** in our Acronym Attic

- Abbreviation Database Surfer
- « Previous
- Next »

Other classical validation parameters (Loague and Green 1991) such as maximum error and root mean **square error** are also reported in this study.

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).

In this study, the neural network training function of the t sigmoid functions and tangent hyperbolic in the MLP, the acceptable application performance in similar processes Is used and the results are compared To determine the best number of hidden layer neurons is required The root mean **square error** of network output Count the number of neurons for each hidden layer neurons is selected in a drawing graphs Root mean **square error** is the lowest number of neurons as the number of neurons in the hidden layer is selected To determine the number of hidden layers in a network similar to this should be done.

For uniform dither theoretical dependence of root mean **square error** (RMSE) upon standard deviation of added noise has been proved through measurements in the whole range.

The normalized mean **square error** (NMES) has been calculated by using Equation 3.

While table-3 gives the root mean **square error** (RMSE) and mean bias error (MBE).

However, the PLS1 models with lower root mean **square error** of prediction (RMSEP), standard error of prediction (SEP) and Bias were better when compared to the PCR models developed using the whole NIR spectra region and restricted NIR spectra region not associated with the hydroxyl band.

Figure 1 shows the root mean **square error** (RMSE) of projections for PSND as a per cent of GDP implied by the same set of stochastic simulations used to assess the uncertainty of projections for the current budget balance.