An Algorithm for Optimally Fitting a Wiener Model

Lucas P. Beverlin, Derrick K. Rollins, Nisarg Vyas and David Andre

Mathematical Problems in Engineering, 2011, vol. 2011, 1-15

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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:570509

DOI: 10.1155/2011/570509

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