A comparison of a neural network and an observer approach for detecting faults in a benchmark system

D. N. Shields, Stephanie Du Four, Elena Gaura

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

The detection of faults is considered for a class of nonlinear systems. A fault detection observer approach is compared to a neural network approach. Both approaches axe applied to a an experimental ( benchmark) three-tank system.

Original languageEnglish
Title of host publicationArtificial Neural Nets and Genetic Algorithms
Subtitle of host publicationProceedings of the International Conference in Prague, Czech Republic, 2001
EditorsVera Kurkova, Nigel C. Steele, Roman Neruda, Miroslav Karny
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages157-160
Number of pages4
ISBN (Electronic)9783709162309
ISBN (Print)3211836519, 9783211836514
DOIs
Publication statusPublished - 2001
Externally publishedYes
EventInternational Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA) - Prague, Czech Republic
Duration: 22 Apr 200125 Apr 2001

Publication series

NameSpringer Computer Science
PublisherSpringer-Verlag Wien

Conference

ConferenceInternational Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA)
CountryCzech Republic
CityPrague
Period22/04/0125/04/01

Keywords

  • NONLINEAR-SYSTEMS

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