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Whenever regression models are optimised, it is important that all optimisation steps are properly validated.
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Predictive models were optimised when variable reduction was employed to further truncate spectra to 1,180 to 900 cm-1 wavenumbers.
All models were optimised by a grid search of P-value cutoffs (0.01, 5E-02, 1E-02, 5E-03, 1E-03, 5E-04,1E-04) and the cutoff resulting in a gene signature of optimal performance was used in the final model.
Modelling was performed using the graphics program O Joness and Kjeldgaard, 1997), the models were optimised using the energy minimisation procedure embedded in the program CNS (Brünger and Rice, 1997), and the illustration was made using programs Bobscript (Esnouf, 1997) and Raster3D (Merritt and Bacon, 1997).
The coefficients of the semi-active model are optimised to maximise the harvested power.
Modelling errors can occur when using PPC data for parametrisation since the model is optimised over a narrower bandwidth than the validation profile.
The performance of the model was optimised by calibrating different parameter values.
The model was optimised to match both the HPMC tablet radius and the concentration profiles over time.
In this work the influence of R on the fatigue crack propagation resistance in a PM duplex stainless steel was investigated and an artificial neural network based model was optimised as a new simulation tool.
The parameters for each model were optimised to give the lowest mean squared error.
Whilst a 40-layer model was optimised in the work of Carter et al.[32], this made use of a mixed atom pseudo-potential and is not explicitly comparable to the models presented here.
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