This is achieved by penalizing the sum of squared errors by the integral of the squared derivative of f, at a rate determined by a smoothing parameter, λ [ 18].
Robust regression replaces the squared error by other error measures reducing the influence of outliers [ 51].
The parameter optimization was executed by minimizing the sum of squares of the errors between simulation data and measured data considering biomass, rhamnolipid and glucose.
*: significant P-value for 95% confidence limits, †: degrees of freedom for each source of variation, ‡: sum of squared distances for each source of variation, ∥: model mean square divided by the error mean square In the second part of this work we have analysed the relationship between nBGT levels and gene evolution rates in Buchnera.
The test statistic F is equal to the 'between treatments' mean square divided by the error mean square.
STEP 2: Estimate the disparity of each layer {Δ p1, Δp2,... Δp N } by minimising the squared error along the EPI lines.
The F-ratio obtained from the ANOVA is the quotient of the model mean squared divided by the error mean squared.
No restriction is imposed on the level of damping present and estimates are acquired by minimizing the square of the error between observed responses and those predicted by a linearized model.
The problem of FOPID-PSS design is transformed as an optimization problem based on performance indices (PI), including Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral of the Time-Weighted Absolute Error (ITAE) and Integral of Time multiplied by the Squared Error (ITSE), where, BA is employed to obtain the optimal stabilizer parameters.
For nonlinearly separable classes linear classifiers were optimally designed, for example, by minimizing the squared error.
