Data mining techniques for improving the reliability of system identification

被引：54

作者：

Saitta, S ^{[1
]}

Raphael, B ^{[1
]}

Smith, IFC ^{[1
]}

机构：

[1] Ecole Polytech Fed Lausanne, IMAC, CH-1015 Lausanne, Switzerland

来源：

ADVANCED ENGINEERING INFORMATICS | 2005年 / 19卷 / 04期

基金：

美国国家科学基金会;

关键词：

model-based diagnosis; system identification; data mining; DAMAGE ASSESSMENT; UPDATING MODELS; UNCERTAINTIES;

D O I：

10.1016/j.aei.2005.07.005

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：289 / 298

页数：10

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