On the Selection of Variables for Qualitative Modelling of Dynamical Systems

Josep Maria MIRATS I TUR, François E. CELLIER, Rafael M. HUBER, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

8 Citations (Scopus)


Behavioural modelling of physical systems from observations of their input/output behaviour is an important task in engineering. Such models are needed for fault monitoring as well as intelligent control of these systems. The paper addresses one subtask of behavioural modelling, namely the selection of input variables to be used in predicting the behaviour of an output variable. A technique that is well suited for qualitative behavioural modelling and simulation of physical systems is Fuzzy Inductive Reasoning (FIR), a methodology based on General System Theory. Yet, the FIR modelling methodology is of exponential computational complexity, and therefore, it may be useful to consider other approaches as booster techniques for FIR. Different variable selection algorithms: the method of the unreconstructed variance for the best reconstruction, methods based on regression coefficients (OLS, PCR and PLS) and other methods as Multiple Correlation Coefficients (MCC), Principal Components Analysis (PCA) and Cluster analysis are discussed and compared to each other for use in predicting the behaviour of a steam generator. The different variable selection algorithms previously named are then used as booster techniques for FIR. Some of the used linear techniques have been found to be non-effective in the task of selecting variables in order to compute a posterior FIR model. Methods based on clustering seem particularly well suited for pre-selecting subsets of variables to be used in a FIR modelling and simulation effort.
Original languageEnglish
Pages (from-to)435-467
Number of pages33
JournalInternational Journal of General Systems
Issue number5
Publication statusPublished - 2002
Externally publishedYes

Bibliographical note

The research reported in this article was made possible, thanks to a Ph.D. fellowship of the Ministry for Education and Culture from the Spanish Government funded within the frame of the TAP96-0882 project that enabled the first author of this paper to spend 3 months at the University of Texas in Austin with the research group of Dr Joe Qin during the fall of 1998.


  • Behavioural modelling
  • Fuzzy inductive reasoning
  • Inductive modelling
  • Input/output modelling
  • Qualitative modelling
  • Variable selection


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