Artículos de revistas
Application of artificial neural networks to the classification of soils from Sao Paulo state using near-infrared spectroscopy
Registro en:
Analyst. Royal Soc Chemistry, v. 126, n. 12, n. 2194, n. 2200, 2001.
0003-2654
WOS:000172510300018
10.1039/b107533k
Autor
Fidencio, PH
Ruisanchez, I
Poppi, RJ
Institución
Resumen
This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%). 126 12 2194 2200