Confidence estimation of the multi-layer perceptron and its application in fault detection systems

被引：39

作者：

Mrugalski, Marcin ^{[1
]}

Wituak, Marcin ^{[1
]}

Korbicz, Jozef ^{[1
]}

机构：

[1] Univ Zielona Gora, Inst Control & Computat Engn, PL-65246 Zielona Gora, Poland

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2008年 / 21卷 / 06期

关键词：

system identification; model uncertainty; neural networks; bounded-error modelling; non-linear systems; robust fault diagnosis; application;

D O I：

10.1016/j.engappai.2007.09.008

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an identification method based on artificial neural networks, which can be used for the robust fault detection. In particular, a problem of the multi-layer perceptron neural network uncertainty estimation with application of the outer bounding ellipsoid algorithm is considered. The mathematical description of the model uncertainty enables designing robust fault detection system, which effectiveness was verified with the DAMADICS benchmark. (c) 2007 Elsevier Ltd. All rights reserved.

引用

页码：895 / 906

页数：12

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