Artificial Neural Networks for Classifying Olfactory Signals

TitelArtificial Neural Networks for Classifying Olfactory Signals
Publication TypeJournal Article
Year of Publication2000
AuthorsLinder R., Pöppl S.J.
JournalStudies in health technology and informatics
Volume77
Pages1220-5
Date Published2000
Publication Languageeng
ISSN0926-9630
SchlüsselwörterAlgorithms, Artificial Intelligence, Humans, Neural Networks (Computer), Odors, Smell, Software
Abstract

For practical applications, artificial neural networks have to meet several requirements: Mainly they should learn quick, classify accurate and behave robust. Programs should be user-friendly and should not need the presence of an expert for fine tuning diverse learning parameters. The present paper demonstrates an approach using an oversized network topology, adaptive propagation (APROP), a modified error function, and averaging outputs of four networks described for the first time. As an example, signals from different semiconductor gas sensors of an electronic nose were classified. The electronic nose smelt different types of edible oil with extremely different a-priori-probabilities. The fully-specified neural network classifier fulfilled the above mentioned demands. The new approach will be helpful not only for classifying olfactory signals automatically but also in many other fields in medicine, e.g. in data mining from medical databases.

PubMed Link

http://www.ncbi.nlm.nih.gov/pubmed/11187516?dopt=Abstract

Alternate JournalStud Health Technol Inform
Erstellt am 19. November 2012 - 15:51 von Kulbe.

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