IJPAM: Volume 104, No. 1 (2015)


Simone S.F. Souza$^1$, Fernando P.A. Lima$^2$, Fábio R. Chavarette$^3$
$^{1,2}$Electrical Engineering Department
UNESP - Univ. Estadual Paulista
Brazil Avenue, 56, 15385-000, Ilha Solteira, SP, BRAZIL
$^3$Department of Mathematics
UNESP - Univ. Estadual Paulista
Brazil Avenue, 56, 15385-000, Ilha Solteira, SP, BRAZIL

Abstract. This work presents a comparative study of three unsupervised data clustering techniques used to perform the monitoring of the structural integrity of an agricultural tractor. The techniques used in this study are: K-Means, Fuzzy C-Means and Kohonen artificial neural network. These techniques are intelligent learning tools, which provide a classification of the information based on the similarity clustering. The main application of these tools is to assist in structures inspection process in order to identify and characterize flaws as well as assist in making decisions, avoiding accidents. To evaluate these algorithms the modeling was performed and signs of simulation from a numerical model of an agricultural tractor. The results obtained by the methodologies presented a comparative study.

Received: July 24, 2015

AMS Subject Classification: 68W40

Key Words and Phrases: monitoring of structural integrity, K-means, fuzzy C-means, Kohonen neural network

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DOI: 10.12732/ijpam.v104i1.10 How to cite this paper?

International Journal of Pure and Applied Mathematics
ISSN printed version: 1311-8080
ISSN on-line version: 1314-3395
Year: 2015
Volume: 104
Issue: 1
Pages: 119 - 133

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CC BY This work is licensed under the Creative Commons Attribution International License (CC BY).