DOI:
https://doi.org/10.14483/2322939X.4696Publicado:
2013-10-15Número:
Vol. 10 Núm. 1 (2013)Sección:
Actualidad TecnológicaESTIMACIÓN DEL PORCENTAJE DE GRASA CORPORAL MEDIANTE REDES NEURONALES
Palabras clave:
Redes neuronales, MLP, SOM, grasa corporal, MATLAB. (es).Descargas
Resumen (es)
En este artículo se presenta la estimación del porcentaje de grasa corporal usando redes neuronales. Se exploran dos métodos distintos para la clasificación y la estimación, el de MLP y el de SOM. Mostrando la metodología usada en cada uno y el mejor resultado obtenido, para finalmente comparar los resultados logrados y concluir.
Referencias
M. T. R..; Barbosa, Manuel R.; Amaral, Chouzal, Teresa María F. Neural networks based approach to estimate body fat (%bf). Porto: Faculdade de Engenharia da Universidade do Porto. 2010.
D. R. A. N. Revathy. Neural network based regression model for accurate estimation of human body fat – obesity assessment using circumference measures. International Journal of Computer and Network Security. (2) 10. 2010.
Leite Pereira, Emilson & De Souza Filho, Carlos Roberto. Mapas auto organizáveis aplicados aomapeamento do potencial mineral naregião de serra les te, provincia mineral de Carajás. Rev. Bras. Geof. [online], (28) 3. 2010,
Wehrens, Ron; Buydens, Lutgarde. Selfand Superorganizing Maps in R: The kohonenPackage. Journal of Statistical Software,(20) 5. October, 2007
Kuen-Chang Hsieh, Yu-Jen Chen, Hsueh- Kuan Lu, Ling- Chun Lee, Yong-Cheng Huang and Yu-Yawn Chen. The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly Hsieh et al. Nutrition Journal. 2013.
X. R. Cui, M. F. Abbod, Q. Liu, Jiann- ShingShieh, T. Y. Chao, C. Y. Hsieh, Y. C. Yang. Ensembled artificial neural networks to predict the fitness score for body composition analysis. The journal of nutrition, health & aging, (15) 5, pp 341-348. May, 2011.
Khosravi, Abbas. SaeidNahavandi, Doug Creighton, & F.Atiya, Amir A comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions On Neural Network, December, 2010.
Jure Zupan. Introduction to artificial neural network (ann) methods: what they are and how to use them. Spain: Department of Chemistry, University Rovira i Virgili Tarragona. 1994.