Numerical Methods of Optimum Experimental Design Based on a Second-Order Approximation of Confidence Regions

Numerical Methods of Optimum Experimental Design Based on a Second-Order Approximation of Confidence Regions

A successful application of model-based simulation and optimization of dynamic processes requires an exact calibration of the underlying mathematical models. Here, a fundamental task is the estimation of unknown and nature given model coefficients by means of real observations. After an appropriat...

Whakaahuatanga katoa

I tiakina i:
Ngā taipitopito rārangi puna kōrero
Kaituhi matua: Nattermann, Max
Ētahi atu kaituhi: Kostina, Ekaterina (Prof. Dr.) (BetreuerIn (Doktorarbeit))
Hōputu: Dissertation
Reo:Ingarihi
I whakaputaina: Philipps-Universität Marburg 2014
Ngā marau:
Sensitivity Analysis
Robuste Versuchsplanung
DoE
Konfidenzgebiete
Sensitivitätsanalyse
Parameter Estimation
Parameterschätzung
Mathematik
Robustification
Confidence Regions
Optimum Experimental Design
Optimale Versuchsplanung
Mathematics
Urunga tuihono:Kuputuhi katoa PDF
Tags: Tāpirihia he Tūtohu
Keine Tags, Fügen Sie den ersten Tag hinzu!

Ipurangi

Kuputuhi katoa PDF

Ngā taipitopito puringa mai i
Tau karanga: urn:nbn:de:hebis:04-z2015-00540
Rā whakaputa: 2015-02-23
Datum der Annahme: 2014-10-27
Downloads: 71 (2024), 116 (2023), 122 (2022), 262 (2021), 195 (2020), 133 (2019), 108 (2018)
Lizenz: https://rightsstatements.org/vocab/InC-NC/1.0/
URL Uru: https://archiv.ub.uni-marburg.de/diss/z2015/0054
https://doi.org/10.17192/z2015.0054

Ngā tūemi rite