Software, Data &
Background Material
Nonlinear System Identification Benchmark Platform
A list of hosted nonlinear dynamical system datasets can be found on nonlinearbenchmark.org. Over 12 system datasets are featured, examples include an industrial robot arm, an electro-magnetical positioning system, and a cascaded tanks system with overflow.
The website acts also as the platform for the yearly workshop and Ph.D. mini-course on nonlinear system identification.
DeepSI
DeepSI offers a powerful Python toolbox to perform Deep System Identification (DeepSI) with a wide range of tools and methods.
The deepSI Python module aims to offer an intuitive machine learning for system identification environment without the need for deep expert knowledge. Implementing a system identification task often requires effectively no more than 10 lines of code. Coding examples can be found on the GitHub page of the toolbox (https://github.com/GerbenBeintema/deepSI).
Multiple projects have been making use of this toolbox, some examples include:
Identification of nonlinear state-space models starting from input-output data (link)
System identification using video data (link)
Identification of steering dynamics in an autonomous car (link)
Identification of Koopman representations of nonlinear systems starting from input-output data only (link)
You can download the toolbox here: https://github.com/GerbenBeintema/deepSI
Please cite this toolbox as:
Gerben Beintema, Roland Toth, Maarten Schoukens. Nonlinear State-Space Identification using Deep Encoder Networks; Proceedings of the 3rd Conference on Learning for Dynamics and Control, PMLR 144:241-250, 2021. Github, Published Version
Gerben Beintema, Roland Toth, Maarten Schoukens; Nonlinear State-space Model Identification from Video Data using Deep Encoders; IFAC-PapersOnLine, vol.50 n7, pp:697-701, doi: 10.1016/j.ifacol.2021.08.442. Github, Published Version
Other Material
Below I include some links to other relevant material linked to (nonlinear) system identification. This material is not authored by me or my research group.
Yearly week-long Ph.D. Spring School on Spring School On Data-Driven Model Learning for Dynamic Systems, organized by Xavier Bombois (CNRS, Laboratoire Ampère), Hugues Garnier (Université de Lorraine, CRAN), Marion Gilson (Université de Lorraine, CRAN) and Guillaume Mercère (Université de Poitiers, LIAS).