Abstract
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.
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Saitta, S., Raphael, B., Smith, I.F.C. (2006). Combining Two Data Mining Methods for System Identification. In: Smith, I.F.C. (eds) Intelligent Computing in Engineering and Architecture. EG-ICE 2006. Lecture Notes in Computer Science(), vol 4200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11888598_54
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DOI: https://doi.org/10.1007/11888598_54
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