Robust model-based fault diagnosis using neural nonlinear estimators

被引：0

作者：

Alessandri, A ^{[1
]}

Baglietto, M ^{[1
]}

Parisini, T ^{[1
]}

机构：

[1] CNR, IAN, Natl Res Council, I-16149 Genoa, Italy

来源：

PROCEEDINGS OF THE 37TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4 | 1998年

关键词：

robust nonlinear fault diagnosis; Neural Networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Robust model-based fault-diagnosis for nonlinear discrete-time systems is addressed in this paper, basing on a novel class of sliding-window state estimators. A rigorous convergence analysis is performed allowing the computation of residual thresholds when modelling errors are present. The use of neural networks is introduced as reliable functional approximators, thus allowing an on-line application of the proposed robust FD scheme.

引用

页码：72 / 77

页数：6

共 16 条

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