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EGICE
2006

Combining Two Data Mining Methods for System Identification

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Combining Two Data Mining Methods for System Identification
System identification is an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, instead of optimizing values of parameters within one behavior model, system identification is supported by multi-model reasoning strategies. The objective of this work is to develop a data mining algorithm that combines principal component analysis and k-means to obtain better understandings of spaces of candidate models. One goal is to improve views of model-space topologies. The presence of clusters of models having the same characteristics, thereby defining model classes, is an example of useful topological information. Distance metrics add knowledge related to cluster dissimilarity. Engineers are thus better able to improve decision making for system identification and downstream tasks such as further measurement, preventative maintenance and structural replacement.
Sandro Saitta, Benny Raphael, Ian F. C. Smith
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where EGICE
Authors Sandro Saitta, Benny Raphael, Ian F. C. Smith
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