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Modeling and Optimization of a Robust Gas Sensor

  • In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model. The models are build on industrial data for the development of a robust gas sensor. The data contain limited amount of samples and a high variance. The mean square error of the models implemented in a test dataset is used as the comparison strategy. The results indicate that standard linear regression approaches as well as Kriging and GP show good results, whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.

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Metadaten
Author:Margarita A. Rebolledo C., Sebastian Krey, Thomas Bartz-BeielsteinGND, Oliver Flasch, Andreas Fischbach, Jörg Stork
URN:urn:nbn:de:hbz:832-cos4-3399
Series (Serial Number):CIplus (3/2016)
Document Type:Report
Language:English
Year of Completion:2016
Release Date:2016/03/15
Tag:Bayesian Learning; Regression
GND Keyword:Lineare Regression; Sensortechnik; Soft Computing
Pagenumber:14
Institutes and Central Facilities:Fakultät für Informatik und Ingenieurwissenschaften (F10)
CCS-Classification:J. Computer Applications / J.2 PHYSICAL SCIENCES AND ENGINEERING / Engineering
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
JEL-Classification:C Mathematical and Quantitative Methods / C9 Design of Experiments / C90 General
Open Access:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung