NLSF

AcronymDefinition
NLSFNetworked Learning Support Framework (UK)
NLSFNonlinear Least Squares Fitting (mathematics)
NLSFNational Longitudinal Study of Freshman
NLSFNon-Lethal Slippery Foam
NLSFNewfoundland and Labrador Snowmobile Federation Inc (Canada)
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References in periodicals archive ?
To begin, the NLSF method requires an initial condition, [x.sub.0], from which to start iteratively solving towards a solution [x.sup.*].
The MATLAB code for the implementation of this NLSF process to solve for the parameters for model one is given in Algorithm 1.
The averaged component parameters (and their standard deviations) returned by the NLSF process for each model are given in Table 1.
To verify that the NLSF process (solving using multiple randomly generated initial conditions) converged to a similar solution for each circuit parameter of each model, it was applied 20 times for each model and the least squares error (LSE) of each was monitored.
For further comparison of the parameter differences from each of the 20 NLSF trials, the values from each trial are given in Figures 7(b), 7(d), and 7(f) for models one, two, and three, respectively.
ALGORITHM 1: MATLAB implementation of NLSF using Isqcurvefit with multiple initial conditions.
An unscreened-SQI using the PCA and EO selected minimum datasets showed the highest SQI values for the [II.sup.L] (homothetic transformation method) and [I.sup.N] (CurveExpert method of NLSF) models used to compare the eight LUSMS (Tables 5 and 6).
For both PCA and EO databases, NLSF methods resulted in higher SQI values than LSF (Tables 5 and 6).
Using NLSF, PCA-SQI values were greater than unscreened-SQI values but with LSF methods unscreened-SQI values were higher.
[16] reported that EO and PCA dataset selection methods produced almost similar SQI values, but PCA-SQI values using PCA and NLSF methods in this study were higher.
Many studies have shown that NLSF resulted in higher SQI values than LSF 11,16], but a sensitivity analysis showed that the [I.sup.L] method explained variation better than the NLSF method for both indexing methods.
In future debates over how much of a preference to give minority college applicants, the NLSF will be able to shed light on a number of murky questions: Do black students ill prepared in ghetto schools learn more from the hard courses of selective colleges or is it better to let them go to colleges that don't usually demand high SAT scores?