TY - GEN
T1 - Predicción de la calidad en leche fresca usando Redes Neuronales artificiales y Regresión multivariable
AU - Oblitas-Cruz, Jimy
AU - Cieza-Rimarachin, Yuleyci
N1 - Publisher Copyright:
© 2023 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2023
Y1 - 2023
N2 - The objective of this research was to compare the best structure of a Neural Network (ANN) with a multivariate nonlinear regression model (MNLR) to predict the physicochemical quality parameters of milk. To create a predictor model for the livestock sector, 3 input and 6 output variables were used. To achieve this, a Feedforward ANN with Backpropagation training algorithms was applied. For the models, the Matlab 2020a software was used. The lowest mean absolute deviation (MAD) was found to be 0.00715952, corresponding to a Neural Network with 2 hidden layers (18 and 19), with Tansig and log sig type function, respectively. MNLR models had R2 values greater than 0.9. Cross-Validation with 10 interactions was used for this purpose. For comparison, a Duncan test was used where it was found that there are no statistically significant differences between the real sample, the MNLR, and the ANN, with a 95.0% confidence level.
AB - The objective of this research was to compare the best structure of a Neural Network (ANN) with a multivariate nonlinear regression model (MNLR) to predict the physicochemical quality parameters of milk. To create a predictor model for the livestock sector, 3 input and 6 output variables were used. To achieve this, a Feedforward ANN with Backpropagation training algorithms was applied. For the models, the Matlab 2020a software was used. The lowest mean absolute deviation (MAD) was found to be 0.00715952, corresponding to a Neural Network with 2 hidden layers (18 and 19), with Tansig and log sig type function, respectively. MNLR models had R2 values greater than 0.9. Cross-Validation with 10 interactions was used for this purpose. For comparison, a Duncan test was used where it was found that there are no statistically significant differences between the real sample, the MNLR, and the ANN, with a 95.0% confidence level.
KW - Artificial Neural Network
KW - Milk Quality
KW - Nonlinear Multivariate Regression
UR - https://www.scopus.com/pages/publications/85172297985
M3 - Contribución a la conferencia
AN - SCOPUS:85172297985
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - Proceedings of the 21st LACCEI International Multi-Conference for Engineering, Education and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Texier, Jose
A2 - Matta, Rodolfo Andres Rivas
T2 - 21st LACCEI International Multi-Conference for Engineering, Education and Technology, LACCEI 2023
Y2 - 19 July 2023 through 21 July 2023
ER -