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• W2017877881 abstract "This article describes the selection of the training algorithm of an artificial neural network (ANN) used to model the drying of olive oil mill waste in a fluidized-bed dryer. The ANN used was a three-layer perceptron that predicts the moisture value at time t + T from experimental data (solid moisture, input air, and fluidized-bed temperature) at t time; T is the sampling time. In this study, 14 training algorithms were tested. This selection was carried out by applying several statistical tests to the real and predicted moisture values. Afterward, an experimental design was carried out to analyze the influence of the training function parameters on the ANN performance. Finally, Polak−Ribiere conjugate gradient backpropagation was selected as the best training algorithm. The ANN trained with the selected algorithm predicted the moisture with a mean prediction error of 1.6% and a correlation coefficient of 0.998." @default.
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• W2017877881 date "2008-08-01" @default.
• W2017877881 modified "2023-10-06" @default.
• W2017877881 title "Optimization of an Artificial Neural Network by Selecting the Training Function. Application to Olive Oil Mills Waste" @default.
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• W2017877881 doi "https://doi.org/10.1021/ie8001205" @default.
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