Machine Learning meets System Identification!
© 2024 EPFL
Our collaborative paper, "Stable Linear Subspace Identification: A Machine Learning Approach", got accepted for ECC2024 (European Control Conference) in Stockholm!
The Big Idea: Introducing SIMBa
This research bridges the gap between ML and linear system identification (SI) with SIMBa. It's a novel method for identifying stable systems using machine learning techniques.
Why SIMBa Matters
SIMBa consistently outperforms traditional methods, especially those with stability guarantees. This paves the way for advancements in various fields!
Open-Source and Ready to Explore
We've open-sourced SIMBa on GitHub (https://github.com/Cemempamoi/simba) for everyone to explore and apply!
See you in Stockholm!
#ECC2024 #MachineLearning #SystemIdentification #OpenSource #Research
This research is supported by the Swiss National Science Foundation under the NCCR Automation (grant agreement 51NF40 180545).