Herkennen van visuele aspecten van infraroodspectra met neurale netwerken

2.50
Hdl Handle:
http://hdl.handle.net/10029/257504
Title:
Herkennen van visuele aspecten van infraroodspectra met neurale netwerken
Authors:
Reinders A; Visser T; Roos D; Vink PJF de; Luinge HJ
Other Titles:
Recognizing visual aspects of infrared spectra with neural networks
Abstract:
Abstract niet beschikbaar

This document describes how neural networks can be trained to classify and recognize infrared spectra. Backpropagation was used as the neural network type. The effect of noise on the recognition capabilities of a network has been investigated by generating 150 spectra with various noise levels out of 3 standard spectra. The trained network appeared to be capable of recognizing spectra correctly up to a noise level of 70%. Recognition appears to be correct up to a noise lebel of 70%. The classifying capabilities of backpropagation of spectra have been studied by training a network with 30 spectra, equally divided over three classes. Fourteen other spectra were used as a control set. Only one spectrum was found to be incorrectly classified. The preliminary conclusion is that neural networks are a useful addition to standard pattern matching techniques, especially for recognizing visual aspects.
Issue Date:
30-Sep-1991
URI:
http://hdl.handle.net/10029/257504
Additional Links:
http://www.rivm.nl/bibliotheek/rapporten/421504001.html
Type:
Onderzoeksrapport
Language:
nl
Sponsors:
RIVM
Appears in Collections:
RIVM official reports

Full metadata record

DC FieldValue Language
dc.contributor.authorReinders A-
dc.contributor.authorVisser T-
dc.contributor.authorRoos D-
dc.contributor.authorVink PJF de-
dc.contributor.authorLuinge HJ-
dc.date.accessioned2012-12-12T15:41:13Z-
dc.date.available2012-12-12T15:41:13Z-
dc.date.issued1991-09-30-
dc.identifier421504001-
dc.identifier.urihttp://hdl.handle.net/10029/257504-
dc.description.abstractAbstract niet beschikbaarnl
dc.description.abstractThis document describes how neural networks can be trained to classify and recognize infrared spectra. Backpropagation was used as the neural network type. The effect of noise on the recognition capabilities of a network has been investigated by generating 150 spectra with various noise levels out of 3 standard spectra. The trained network appeared to be capable of recognizing spectra correctly up to a noise level of 70%. Recognition appears to be correct up to a noise lebel of 70%. The classifying capabilities of backpropagation of spectra have been studied by training a network with 30 spectra, equally divided over three classes. Fourteen other spectra were used as a control set. Only one spectrum was found to be incorrectly classified. The preliminary conclusion is that neural networks are a useful addition to standard pattern matching techniques, especially for recognizing visual aspects.en
dc.description.sponsorshipRIVM-
dc.format.extent27 p-
dc.language.isonl-
dc.relation.ispartofRIVM Rapport 421504001-
dc.relation.urlhttp://www.rivm.nl/bibliotheek/rapporten/421504001.html-
dc.subject20nl
dc.subject91-3nl
dc.subjectinfraroodspectranl
dc.subjectneurale netwerkennl
dc.subjectbackpropagationnl
dc.subjectpatroonherkenningnl
dc.subjectinfrared spectranl
dc.subjectneural networksnl
dc.subjectpattern recognitionnl
dc.titleHerkennen van visuele aspecten van infraroodspectra met neurale netwerkennl
dc.title.alternativeRecognizing visual aspects of infrared spectra with neural networksen
dc.typeOnderzoeksrapport-
dc.date.updated2012-12-12T15:41:14Z-
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