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Artificial Neural Networks for Classifying Olfactory Signals

TitleArtificial Neural Networks for Classifying Olfactory Signals
Publication TypeJournal Article
Year of Publication2000
AuthorsLinder R., Pöppl S.J.
JournalStudies in health technology and informatics
Date Published2000
Publication Languageeng
KeywordsAlgorithms, Artificial Intelligence, Humans, Neural Networks (Computer), Odors, Smell, Software

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


Alternate JournalStud Health Technol Inform
Created at November 19, 2012 - 2:51pm by Kulbe.


Program of Study

Study Medical Informatics
at the University of Lübeck

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