An Algorithm for Optimally Fitting a Wiener Model. (8th December 2011)

• Record Type: Journal Article
• Authors: Beverlin, Lucas P.; Rollins, Derrick K.; Vyas, Nisarg; Andre, David
• Other Names: Macau Elbert Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2011(2011)
• Publication Date: 2011-12-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2011/570509
• Languages: English
• ISSNs: 1024-123X

Abstract

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.