Comparison of Prediction Performances between Models Obtained by the Group Method of Data Handling and Neural Networks for the Alcoholic Fermentation Rate in Enology

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概要

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• The modelling of winemaking processes, to predict as far ahead as possible the fermentation performance, is necessary for enhanced supervision and to enable appropriate corrective action to be taken to remedy incorrect fermentation before it is too late. In this paper, we briefly present two heuristic modelling methods-the Group Method of Data Handling(GMDH)and Neural Networks(NN)-which can be used to obtain unstructured models. The identification and prediction performances of the models obtained with these two methods are compared with respect to the alcoholic fermentation rate(dCO_2/dt)at five prediction horizons and for four fermentations. It is shown that predictive models obtained with neural network methodology are more accurate than those obtained with GMDH. On the other hand, GMDH models are more versatile when used for the prediction of the fermentation rate of a different fermentation than the one used in the learning process.

• 公益社団法人日本生物工学会の論文
• 1991-05-25

著者

• Corrieu Georges

  Laboratoire De Genie Des Procedes Biotechnologiques Agro-alimentaires

• CLERAN YVON

  Laboratoire de Genie des Procedes Biotechnologiques Agro-Alimentaires

• THIBAULT JULES

  Department of Chemical Engineering, Laval University

• CHERUY ARLETTE

  Laboratoire d'Automatique de Grenoble

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• Comparison of Prediction Performances between Models Obtained by the Group Method of Data Handling and Neural Networks for the Alcoholic Fermentation Rate in Enology
• Method for On-Line Prediction of Kinetics of Alcoholic Fermentation in Wine Making

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