I am working on system identification for general dynamic systems.
The idea is that I think it can be well-presented using regression using the input and output data.
Do you know anyone who worked on this before, or maybe, you can direct me to published work of something similar?
Thank you very much
Hello Mohammad Tawfik ,
Are you aware of the books by Norton, Box & Jenkins, or Cryer?
[1] J. P. Norton; An Introduction to Identification; Dover Publucations, Inc.; 1986.
[2] George E. P. Box, Gwilym M. Jenkins; Time Series Analysis, Forecasting and Control; Holden-Day; 1970.
[3] Jonathan D. Cryer; Time Series Analysis; Duxbury Press; 1986.
Regards,
Tom Cuff
Do you mean working with nftool toolbox in MATLAB? You can use input and output data to tarin a neural network (MLP or RBF). Please consider the 'newff' function in MATLAB to change the default settings.
Dear Mohammad Tawfik,
I suggest you to see links and attached files on topic.
https://www.amazon.com/Time-Psychology-R-M-Gregson/dp/089859250X
Conference Paper Hammerstein model identification
Best regards
In addition to the mentioned references, I suggest reading the first chapter of a famous book "System Identification; Theory for the user" (Written by Ljung 1999) on pages 1-12, where you find a brief mathematical introduction to the regression method.
Hello Professor Tawfik,
I hope all is well. I suggest having a look on DMD: https://en.wikipedia.org/wiki/Dynamic_mode_decomposition
http://cwrowley.princeton.edu/theses/tu.pdf
and on Koopmann Analysis:
https://www.springer.com/gp/book/9783030357122
Another recent paper in IEEE TAC: Article Formulas for Data-Driven Control: Stabilization, Optimality,...
I understand that these are not strictly 'regression' methods but they're essentially, in one way or another, least squares methods which better suited for dynamic systems identification/model reduction.
Hello,
These references are pretty great.
Billings, S. A.: Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains, John Wiley & Sons, Ltd, Chichester, UK, 2013.
Nelles, O.: Nonlinear System Identification, Springer Berlin Heidelberg, Berlin, Heidelberg, 2001.
Schoukens, J. and Ljung, L.: Nonlinear System Identification: A User-Oriented Roadmap, arXiv:1902.00683 [cs], https://doi.org/10.1109/MCS.2019.2938121, 2019.J. and Ljung, L.: Nonlinear System Identification: A User-Oriented Roadmap, arXiv:1902.00683 [cs], 2019.
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