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.
Author: | Margarita A. Rebolledo C., Sebastian Krey, Thomas Bartz-BeielsteinGND, Oliver Flasch, Andreas Fischbach, Jörg Stork |
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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: | Soft Computing; Lineare Regression; Sensortechnik |
Page Number: | 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 / 004 Informatik |
JEL-Classification: | C Mathematical and Quantitative Methods / C9 Design of Experiments / C90 General |
Open Access: | Open Access |
Licence (German): | Creative Commons - Namensnennung, Nicht kommerziell, Keine Bearbeitung |