Model Verification and Validation in System Identification

Model Verification and Validation in System Identification. Seminar of two hours for the Nanjing University of Aeronautics and Astronautics - NUAA
 

Abstract

• Model Verification and Validation in System Identification are crucial steps to ensure the reliability and predictive accuracy of mathematical models used for describing dynamic systems. This seminar addresses the foundational concepts, methodologies, and practical tools employed to verify and validate system identification models. Verification, the process of ensuring correct implementation of the intended mathematical structure, and validation, the assessment of the model's capability to represent real-world behaviour, will be clearly distinguished and illustrated. Participants will be guided through various established techniques, including residual analysis, cross-validation, sensitivity analysis, and simulation-based methods. Particular focus will be given to handling common practical challenges such as model uncertainty, noise contamination, and data limitations, especially within nonlinear and dynamic modelling contexts. Real-world case studies will highlight how effective verification and validation processes can significantly enhance decision-making reliability across engineering and industrial applications. The seminar concludes by identifying emerging trends and future directions, including the integration of artificial intelligence and machine learning approaches into verification and validation workflows. This session is designed to equip researchers and practitioners with practical strategies to systematically verify and validate identified models, thus improving robustness, credibility, and performance in practical system identification scenarios.

Keywords

• System identification, model validation, model verification, residual analysis, predictive accuracy.

Talk's Topics

• General Readings. Ljung, L. System Identification: Theory For User. Book in PDF format. 1st Ed. 1987.
• Nonparametric Identification. Input Signals for Identification. Identification Conditions. Lecture Slides PDF Format (1.4MB). Nonparametric Models (part 1) recorded movie (YouTube Channel); Input Signals (part 2) recorded movie (YouTube Channel).
• An Application Example with the System Identification Toolbox of Matlab. Lecture Slides PDF Format (6.5MB). Lecture 5 recorded movie (YouTube Channel).
• Remarks on System Identification and Data Analysis. Lecture Slides PDF Format (2.8MB). Lecture 6 recorded movie (YouTube Channel).
• The System Identification Toolbox by MathWorks. Tutorial from MathWorks Website. Webinar by Lennart Ljung (YouTube Channel). Lecture recorded movie (YouTube Channel).
• Real Chemical SISO Process (CSTR, Prof. Robert Babuska, 1998). Zipped folder (.zip); txt file with a few details. Example of identification of ARX models recorded movie (YouTube Channel).
• Open Problems and Future Works. Some System Identification Challenges and Approaches. Slides in PDF format.

Downloads

• Identification Dataset Example for the System Identification Toolbox of Matlab. Example process data from a SISO model: (Dynamic process data file in ascii format, Matlab Dataset).
• 22. K. Sigmon, "Matlab Primer Third Edition". University of Florida, Florida, Second Edition ed., 1992. (file in formato pdf ).
• Get Started with System Identification Toolbox System Identification Graphical User Interface for Matlab. (Matlab website, English).
• System Identification Toolbox System Identification Graphical User Interface for Matlab. (pdf format file).

General References and Textbooks

• SID, "System Identification Toolbox". System Identification Graphical User Interface for Matlab (pdf format file).
• System Identification: Theory for the User, Lennart Ljung - Springer, 1999.
• Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques, S. Simani, C. Fantuzzi, R. J. Patton - Springer, 2003.
• System Identification, T. Soderstrom, P. Stoica, Prentice Hall International, Cambridge, 1989

Fundamental Publications on System Identification and Data Analysis

Books

1. Ljung, L. (1999). System Identification: Theory for the User (2nd ed.). Prentice Hall, Upper Saddle River, NJ, USA.
2. Ljung, L., Glad, T. (2021). Modeling and Identification of Dynamic Systems. Studentlitteratur AB, Lund, Sweden.
3. Soderstrom, T., Stoica, P. (1989). System Identification. Prentice Hall International, Hemel Hempstead, UK.
4. Sintelon, R., Schoukens, J. (2012). System Identification: A Frequency Domain Approach (2nd ed.). Wiley-IEEE Press, Hoboken, NJ, USA.
5. Nelles, O. (2001). Nonlinear System Identification: From Classical Approaches to Neural Networks. Springer, Berlin, Germany.
6. Goodwin, G.C., Payne, R.L. (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press, New York, NY, USA.

Journal Articles

7. Schoukens, J., Ljung, L. (2019). Nonlinear system identification: A user-oriented roadmap. IEEE Control Systems Magazine, 39(6), 28 - 99.
8. Pillonetto, G., Ljung, L., Chen, T. (2023). Deep networks for system identification: A survey. Annual Reviews in Control, 56, 242 - 264.
9. Wahlberg, B., Ljung, L. (2018). Algorithms and performance analysis for stochastic Wiener system identification. Automatica, 95, 277 - 289.
10. Brunton, S.L., Proctor, J.L., Kutz, J.N. (2016). Sparse Identification of Nonlinear Dynamical Systems with Control (SINDYc). IFAC-PapersOnLine, 49(18), 710 - 715.
11. Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L. (2014). Kernel methods in system identification, machine learning and function estimation: A survey. Automatica, 50(3), 657 - 682.
12. Ljung, L. (2010). Perspectives on system identification. Annual Reviews in Control, 34(1), 1 - 12.
13. Verdult, V., Ljung, L. (2005). Nonlinear state-space system identification with application to neural networks. Automatica, 41(11), 1771 - 1784.
14. Ljung, L. (2008). Perspectives on the relation between artificial intelligence and system identification. IFAC Proceedings Volumes, 41(2), 1 - 12.
15. Gevers, M., Ljung, L. (1986). Optimal experiment designs with respect to the intended model application. Automatica, 22(5), 543 - 554.

Software Resources

16. Ljung, L. (2024). System Identification Toolbox (version 10.1). MathWorks, Natick, MA, USA. (PDF format file).

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