2016
DOI: 10.1016/j.bspc.2016.06.009
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A modified approach to objective surface generation within the Gauss-Newton parameter identification to ignore outlier data points

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Cited by 13 publications
(9 citation statements)
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“…(6*) Gray et al (2015) modified Eq. 6 by modulating the magnitude of the residual vector and thus changing the weighting given to each data point in the iterations.…”
Section: Methodsmentioning
confidence: 99%
“…(6*) Gray et al (2015) modified Eq. 6 by modulating the magnitude of the residual vector and thus changing the weighting given to each data point in the iterations.…”
Section: Methodsmentioning
confidence: 99%
“…Points with high model error due to unmodelled mixing dynamics can lead to inaccurate parameter identification. 11,12 In cases where parameter identification may be ill posed, appropriate use of penalty functions and/or regularization methods may improve convergence. 13,14 One method for dealing with unmodelled mixing behavior is to exclude sampling data for the 5 or 10 minute period following bolus administration as it can be assumed it takes this length of time for glucose and insulin boluses to adequately mix.…”
Section: Introductionmentioning
confidence: 99%
“…13,14 One method for dealing with unmodelled mixing behavior is to exclude sampling data for the 5 or 10 minute period following bolus administration as it can be assumed it takes this length of time for glucose and insulin boluses to adequately mix. 2,15 Another method adapted the Gauss-Newton gradient-descent parameter identification method to limit the influence of outliers, 12 where subsequent comparison of identified insulin sensitivity estimates to a standard modelling approach showed it effectively captured model parameters typically obscured by unmodelled mixing dynamics. 16,17 A third approach is to include an additional local mixing compartment in the model.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Curve fitting is one of the most powerful and most widely used analysis tools to pre- and post-process data [ 1 , 2 ], to remove outliers [ 3 , 4 ], to compare candidate models [ 5 ], and to examine the relationship between one or more predictors [ 6 , 7 , 8 ]. The parameterization of curve fitting is usually realized by minimizing linear or non-linear least-squares residuals [ 9 , 10 ], in which the Levenberg–Marquardt (LM) algorithm, also known as the damped least-squares method, is popular for fast convergence [ 11 ]. Whether the LM fitting results can reflect physical meaning is highly dependent on the quality of measured signal.…”
Section: Introductionmentioning
confidence: 99%