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dc.contributor.authorIntan Maisarah, Abd Rahim-
dc.contributor.authorFauziah, Mat-
dc.contributor.authorSazali, Yaacob, Prof. Dr.-
dc.contributor.authorRakhmad Arief, Siregar-
dc.date.accessioned2011-10-06T09:33:04Z-
dc.date.available2011-10-06T09:33:04Z-
dc.date.issued2011-03-04-
dc.identifier.citationp. 207-211en_US
dc.identifier.isbn978-161284414-5-
dc.identifier.urihttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5759874-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/14045-
dc.descriptionLink to publisher's homepage at http://ieeexplore.ieee.org/en_US
dc.description.abstractThis paper focused on experimental data and study for the testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for training. As an extension for the study, the system tested with various method of neural network training algorithm. The Levenberg-Marquardt Backpropagation used as the algorithm in an artificial neural network system developeden_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofseriesProceedings of the 7th International Colloquium on Signal Processing and its Applications (CSPA 2011)en_US
dc.subjectFrequency Response Functionen_US
dc.subjectLevenberg-Marquardt Backpropagationen_US
dc.subjectVibration analysisen_US
dc.subjectVibration techniqueen_US
dc.titleClassifying material type and mechanical properties using artificial neural networken_US
dc.typeWorking Paperen_US
dc.contributor.urlumaisarah_138@yahoo.comen_US
dc.contributor.urlfauziah@unimap.edu.myen_US
dc.contributor.urlsazali22@yahoo.comen_US
dc.contributor.urlrakhmadarief@gmail.comen_US
Appears in Collections:Conference Papers
Sazali Yaacob, Prof. Dr.
Rakhmad Arief Siregar, Dr.

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