Data mining techniques for improving the reliability of system identification

Authors:
S. Saitta;B. Raphael;I. F. C. Smith
Affiliations:
Ecole Polytechnique Fédérale de Lausanne (EPFL), Applied computing and mechanics laboratory (IMAC), CH-1015 Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne (EPFL), Applied computing and mechanics laboratory (IMAC), CH-1015 Lausanne, Switzerland;Ecole Polytechnique Fédérale de Lausanne (EPFL), Applied computing and mechanics laboratory (IMAC), CH-1015 Lausanne, Switzerland
Venue:
Advanced Engineering Informatics
Year:
2005

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Abstract

A system identification methodology that makes use of data mining techniques to improve the reliability of identification is presented in this paper. An important aspect of the methodology is the generation of a population of candidate models. Indications of the reliability of system identification are obtained through an examination of the characteristics of the population. Data mining techniques bring out model characteristics that are important. The methodology has been applied to several engineering systems.