Application of artificial neural networks to the classification of soils from Sao Paulo state using near-infrared spectroscopy

This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter pr opagation neural network (CP-ANN) and a radial basis function network (RBFN ). These strategies were used to classify soil samples from different geogr aphical regions in Brazil by means of their near-infrared (diffuse reflecta nce) spectra. The results were better with CP-ANN (classification error 8.6 %) than with RBFN (classification error 11.0%).