Abstract
A new method for modelling of complex processes containing unknown qualitative variables is presented. The method is based on clustering the input-output-pairs of the model into a finite number of clusters each representing a different value of unknown qualitative variables. The clustering algorithm is derived from the assumption that each cluster consists of realizations of a normally distributed random vector. The method has been tested by simulated process model and real data from mineral processes.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Ginsberg, D.W., Whiten, W.J.(1991). A data based expert system for engineering applications. IFAC Workshop on Expert Systems in Mining and Metal Processing, Espoo.
James, M.(1985). Classification Algorithms. Collins, London.
Pulkkinen,K., Ylinen,R., Jämsä-Jounela,S.-L., Järvensivu, M.(1993). Integrated expert system for grinding and flotation. XVIII International Mineral Processing Congress, Sydney.
Ylinen,R., Jämsä-Jounela,S-L.,Miettunen,J.(1993). Use of cluster analysis in process control. XII World Congress of IFAC, Sydney.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ylinen, R. (1994). Cluster-based modelling of processes with unknown qualitative variables. In: Pichler, F., Moreno Díaz, R. (eds) Computer Aided Systems Theory — EUROCAST '93. EUROCAST 1993. Lecture Notes in Computer Science, vol 763. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57601-0_56
Download citation
DOI: https://doi.org/10.1007/3-540-57601-0_56
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57601-3
Online ISBN: 978-3-540-48286-4
eBook Packages: Springer Book Archive