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Machine Learning for Cyber-Physical Systems
- Publication Year :
- 2024
- Publisher :
- Cham: Springer Nature; Springer Nature Switzerland, 2024.
-
Abstract
- This open access proceedings presents new approaches to Machine Learning for Cyber-Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber-Physical Systems, which was held in Hamburg (Germany), March 29th to 31st, 2023. Cyber-physical systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments. This is an open access book.
- Subjects :
- Cyber-physical systems
Neural networks
Computer Science
Network architecture
Automatic validation
Machine learning
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJF Electronics engineering
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general::GPF Information theory::GPFC Cybernetics and systems theory
thema EDItEUR::U Computing and Information Technology::UK Computer hardware
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics::PBWH Mathematical modelling
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-031-47062-2
978-3-031-47061-5
3-031-47062-1
3-031-47061-3 - ISBNs :
- 9783031470622, 9783031470615, 3031470621, and 3031470613
- Database :
- OAPEN Library
- Notes :
- ONIX_20240716_9783031470622_2, , https://link.springer.com/978-3-031-47062-2
- Publication Type :
- eBook
- Accession number :
- edsoap.20.500.12657.92296
- Document Type :
- book
- Full Text :
- https://doi.org/10.1007/978-3-031-47062-2