Neural Model for the Prediction of Volume Losses in the Aging Process of Rums
| Autor: | Luis Eduardo López de la Maza, Lourdes Zumalacárregui de Cárdenas, Osney Pérez Ones, Beatriz García Castellanos, Idania Blanco Carvajal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Coefficient of determination Wilcoxon signed-rank test Mean squared error Degree (temperature) 03 medical and health sciences 0302 clinical medicine matlab Statistics MATLAB Mathematics computer.programming_language Artificial neural network aging modeling General Medicine Engineering (General). Civil engineering (General) 030104 developmental biology 030220 oncology & carcinogenesis Multilayer perceptron rums volume losses TA1-2040 computer artificial neural networks Volume (compression) |
| Zdroj: | Revista Facultad de Ingeniería, Vol 29, Iss 54, Pp e10514-e10514 (2020) |
| ISSN: | 2357-5328 0121-1129 |
| Popis: | The rum aging process shows volume losses, called wastage. The numerical operation variables: product, boardwalk, horizontal and vertical positions, date, volume, alcoholic degree, temperature, humidity and aging time, recorded in databases, contain valuable information to study the process. MATLAB 2017 software was used to estimate volume losses. In the modeling of the rum aging process, the multilayer perceptron neuronal network with one and two hidden layers was used, varying the number of neurons in these between 4 and 10. The Levenberg-Marquadt (LM) and Bayesian training algorithms were compared (Bay) The increase in 6 consecutive iterations of the validation error and 1,000 as the maximum number of training cycles were the criteria used to stop the training. The input variables to the network were: numerical month, volume, temperature, humidity, initial alcoholic degree and aging time, while the output variable was wastage. 546 pairs of input/output data were processed. The statistical Friedman and Wilcoxon tests were performed to select the best neural architecture according to the mean square error (MSE) criteria. The selected topology has a 6-4-4-1 structure, with an MSE of 2.1∙10-3 and a correlation factor (R) with experimental data of 0.9898. The neural network obtained was used to simulate thirteen initial aging conditions that were not used for training and validation, detecting a coefficient of determination (R2) of 0.9961. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|