Combining two data mining methods for system identification

被引:0
作者
Saitta, Sandro [1 ]
Raphael, Benny
Smith, Ian F. C.
机构
[1] Ecole Polytech Fed Lausanne, IMAC, Stn 18, CH-1015 Lausanne, Switzerland
[2] Natl Univ Singapore, Dept Bldg, Singapore 117566, Singapore
来源
INTELLIGENT COMPUTING IN ENGINEERING AND ARCHITECTURE | 2006年 / 4200卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:606 / 614
页数:9
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