UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A model-based data mining approach for determining the domain of validity of approximated models

Quaglio, M; Fraga, E; Cao, E; Gavriilidis, A; Galvanin, F; (2018) A model-based data mining approach for determining the domain of validity of approximated models. Chemometrics and Intelligent Laboratory Systems , 172 pp. 58-67. 10.1016/j.chemolab.2017.11.010. Green open access

[thumbnail of Galvanin_1-s2.0-S0169743917304483-main.pdf]
Preview
Text
Galvanin_1-s2.0-S0169743917304483-main.pdf - Accepted Version

Download (1MB) | Preview

Abstract

Parametric models derived from simplifying modelling assumptions give an approximated description of the physical system under study. The value of an approximated model depends on the consciousness of its descriptive limits and on the precise estimation of its parameters. In this manuscript, a framework for identifying the model domain of validity for the simplifying model hypotheses is presented. A model-based data mining method for parameter estimation is proposed as central block to classify the observed experimental conditions as compatible or incompatible with the approximated model. A nonlinear support vector classifier is then trained on the classified (observed) experimental conditions to identify a decision function for quantifying the expected model reliability in unexplored regions of the experimental design space. The proposed approach is employed for determining the domain of reliability for a simplified kinetic model of methanol oxidation on silver catalyst.

Type: Article
Title: A model-based data mining approach for determining the domain of validity of approximated models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.chemolab.2017.11.010
Publisher version: https://doi.org/10.1016/j.chemolab.2017.11.010
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Model identification, Maximum likelihood, Data mining, Machine learning, Model diagnosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering
URI: https://discovery.ucl.ac.uk/id/eprint/10038346
Downloads since deposit
132Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item