A counterpropagation artificial neural network (CP-ANN) approach was used to classify 1779 Italian rice samples according to their variety, using physical measurements which are routinely determined for the commercial classification of the product. If compared to the classical Principal Component Analysis, the mapping based on the Kohonen network showed a significantly better representational ability, being able to separate classes which appeared quite undistinguished in the PC space. From the classification and prediction viewpoint, the optimal CP-ANN was able to correctly predict more than 90% of the test set samples. (C) 2004 Elsevier B.V. All rights reserved.
On the use of counterpropagation artificial neural networks to characterize Italian rice varieties / Marini, Federico; Jure, Zupan; Magri', Antonio. - In: ANALYTICA CHIMICA ACTA. - ISSN 0003-2670. - STAMPA. - 510:2(2004), pp. 231-240. [10.1016/j.aca.2004.01.009]
On the use of counterpropagation artificial neural networks to characterize Italian rice varieties
MARINI, Federico;MAGRI', Antonio
2004
Abstract
A counterpropagation artificial neural network (CP-ANN) approach was used to classify 1779 Italian rice samples according to their variety, using physical measurements which are routinely determined for the commercial classification of the product. If compared to the classical Principal Component Analysis, the mapping based on the Kohonen network showed a significantly better representational ability, being able to separate classes which appeared quite undistinguished in the PC space. From the classification and prediction viewpoint, the optimal CP-ANN was able to correctly predict more than 90% of the test set samples. (C) 2004 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
