Expressing uncertainty in neural networks for production systems
| Autor: | Bernd Zimmering, Samim Ahmad Multaheb, Oliver Niggemann |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science business.industry 02 engineering and technology 030204 cardiovascular system & hematology Computer Science Applications 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Control and Systems Engineering Production (economics) Anomaly detection Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | at - Automatisierungstechnik. 69:221-230 |
| ISSN: | 2196-677X 0178-2312 |
| DOI: | 10.1515/auto-2020-0122 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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