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Reduced order modelling through system identification using stochastic filtering
This thesis presents a novel approach to model order reduction, through system identification and using stochastic filtering. Order reduction is a particularly relevant application in the automotive context, as the generation of simplified simulation models for the whole vehicle and its subsystems i...
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| Format: | Default Thesis |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.26174/thesis.lboro.8216285.v1 |
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