an algorithm for optimally fitting a wiener model

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ID: 220909
2011
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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 from models 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.
Reference Key
beverlin2011mathematicalan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Lucas P. Beverlin;Derrick K. Rollins;Nisarg Vyas;David Andre
Journal journal of power sources
Year 2011
DOI
10.1155/2011/570509
URL
http://dx.doi.org/10.1155/2011/570509
Keywords
civil engineering (general)mathematics engineering (general)

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