File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Classification of coffee using artificial neural network

TitleClassification of coffee using artificial neural network
Authors
Yip, DHFYu, WWH
KeywordsArtificial Neural Network
Genetic Algorithms
Adaptive Back-Propagation
Issue Date1996
PublisherIEEE.
Citation
IEEE International Conference on Evolutionary Computation Proceedings, Nagoya, Japan, 20-22 May 1996, p. 655-658 How to Cite?
DOI: http://dx.doi.org/10.1109/ICEC.1996.542678
AbstractThe paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive backpropagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has successfully classified the coffees using a relatively small set of training data. The performance of the optimized ANN compare significantly better than the methods proposed by other researchers.
Persistent Identifierhttp://hdl.handle.net/10722/46577

 

DC FieldValueLanguage
dc.contributor.authorYip, DHFen_HK
dc.contributor.authorYu, WWHen_HK
dc.date.accessioned2007-10-30T06:53:18Z-
dc.date.available2007-10-30T06:53:18Z-
dc.date.issued1996en_HK
dc.identifier.citationIEEE International Conference on Evolutionary Computation Proceedings, Nagoya, Japan, 20-22 May 1996, p. 655-658en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46577-
dc.description.abstractThe paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive backpropagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has successfully classified the coffees using a relatively small set of training data. The performance of the optimized ANN compare significantly better than the methods proposed by other researchers.en_HK
dc.format.extent340073 bytes-
dc.format.extent3380 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectArtificial Neural Networken_HK
dc.subjectGenetic Algorithmsen_HK
dc.subjectAdaptive Back-Propagationen_HK
dc.titleClassification of coffee using artificial neural networken_HK
dc.typeConference_Paperen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ICEC.1996.542678en_HK
dc.identifier.hkuros28424-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats