Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/45620
Title: Data mining for decision support in multiple-model system identification
Authors: Saitta, S.
Raphael, B. 
Smith, I.F.C.
Keywords: Clustering
Data mining
Knowledge extraction
System identification
Issue Date: 2006
Source: Saitta, S.,Raphael, B.,Smith, I.F.C. (2006). Data mining for decision support in multiple-model system identification. WSEAS Transactions on Systems 5 (12) : 2795-2800. ScholarBank@NUS Repository.
Abstract: Data mining techniques presented in the literature are usually used for prediction and they are tested on well known benchmark problems. System identification is a practical engineering problem and an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, system identification is supported by multiple-model reasoning strategies. The objective of this work is to study the use of data mining techniques for system identification. 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.
Source Title: WSEAS Transactions on Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/45620
ISSN: 11092777
Appears in Collections:Staff Publications

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