Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/54727
Type: Artigo de periódico
Title: Application of artificial neural networks to the classification of soils from Sao Paulo state using near-infrared spectroscopy
Author: Fidencio, PH
Ruisanchez, I
Poppi, RJ
Abstract: 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%).
Country: Inglaterra
Editor: Royal Soc Chemistry
Rights: aberto
Identifier DOI: 10.1039/b107533k
Date Issue: 2001
Appears in Collections:Artigos e Materiais de Revistas Científicas - Unicamp

Files in This Item:
File Description SizeFormat 
WOS000172510300018.pdf354.78 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.