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Title: Comparison of classifying the material mechanical properties by using k-Nearest Neighbor and Neural Network Backpropagation
Authors: Intan Maisarah, Abd Rahim
Fauziah, Mat
Sazali, Yaacob, Prof. Dr.
???metadata.dc.contributor.url???: umaisarah_138@yahoo.com
fauziah@unimap.edu.my
sazali22@yahoo.com
Keywords: k-Nearest Neighbor (k-NN);Neural network;Material mechanical properties
Issue Date: Mar-2011
Publisher: Science Academy
Citation: International Journal of Research and Reviews in Artificial Intelligence, vol. 1(1), 2011, pages 7-11
Abstract: This paper present a development of a system with non-destructive testing on the material to define the mechanical properties of material. The experimental and testing of the material mechanical properties using vibration technique could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies and its amplitude of the material as the input data needed for training. As an extension for the study, the input data tested with various method of classifier. The k-Nearest Neighbor classifier and artificial neural network with Levenberg-Marquardt Backpropagation are developed to work as a system to classify the materials tested according to their mechanical properties. The result from the classification system shows that k-NN is giving the accuracy of 99.79783 % with the k value of 1 and in the other hand, Levenberg-Marquardt Backpropagation is giving the best classification rate of 99.86%.
Description: Link to publisher's homepage at http://www.sciacademypublisher.com
URI: http://www.sciacademypublisher.com/journals/index.php/IJRRAI/article/view/46/39
http://hdl.handle.net/123456789/12102
ISSN: 2046-5122
Appears in Collections:School of Mechatronic Engineering (Articles)
Sazali Yaacob, Prof. Dr.

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