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An Algorithm for Optimally Fitting a Wiener Model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Beverlin, Lucas P. Rollins, Derrick K. Vyas, Nisarg Andre, David |
| Copyright Year | 2014 |
| Abstract | The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at a time. This methodology is applied to Wiener networks that are used to predict blood glucose concentrations. The predictions of validation sets frommodels fit to four subjects using this methodology yielded a higher correlation between observed and predicted observations than other algorithms, including the Gauss-Newton and Levenberg-Marquardt algorithms. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.maths.tcd.ie/EMIS/journals/HOA/MPE/Volume2011/570509.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Blood Glucose Curve fitting Gauss Gauss–Newton algorithm Iterative method Levenberg–Marquardt algorithm Newton Nonlinear system Overfitting Supervised learning |
| Content Type | Text |
| Resource Type | Article |
