GAおよびFNNを用いた清酒もろみの品温軌道の導出

概要

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Simulation models for Baume and alcohol concentration from the 11th day to the end of the sake mashing were constructed using a fuzzy neural network(FNN). The models could simulate the time courses of Baume and alcohol concentration in 17 actual sake mashings. Average errors at the ends of the mashings were 0.22 and 0.40% for Baume and alcohol concentration, respectively. By applying a genetic algorithm (GA) with the simulation models, temperature time courses were calculated with good accuracy, and the target values for Baume and alcohol concentration on the final day could be achieved. To make a variety of sakes with different qualities, temperature courses were calculated against 3 target values : higher (+0.3), ordinary (0.0), and lower (-0.3) final day Baumes. The calculated temperature courses were found to be similar to a Toji's (expert's) strategy for making decisions on temperature. By applying this procedure, quality control of sake can be realized.
1998-08-25