Title: Expressing uncertainty in neural networks for production systems
Authors: Multaheb, Samim Ahmad
Zimmering, Bernd 
Niggemann, Oliver 
Language: en
Subject (DDC): DDC::000 Informatik, Informationswissenschaft, allgemeine Werke::000 Informatik, Wissen, Systeme
Issue Date: 2021
Document Type: Article
Journal / Series / Working Paper (HSU): Automatisierungstechnik : AT 
Volume: 69
Issue: 3
Page Start: 221
Page End: 230
The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network's ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.
Organization Units (connected with the publication): Informatik im Maschinenbau 
URL: https://api.elsevier.com/content/abstract/scopus_id/85102590202
ISSN: 01782312
DOI: 10.1515/auto-2020-0122
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