MODELING OF THE SENSORY EVALUATION OF SAKE BY DEMPSTER-SHAFERS MEASURE AND GENETIC ALGORITHM

Sensory evaluation data obtained from experts were analyzed by numeric al methods. The aim of this study is to identify a model that can obje ctively estimate the sensory evaluation results based on the concentra tions of components in sake. To this aim, a learning model in which De mpster-Shafer's measure was learned by genetic algorithm (GA) was cons tructed. The learning process was performed by discovery of the assign ments of basic probabilities according to the decrease in error betwee n the observed and estimated data. When the model was compared with ba ck propagation and multiple regression analysis by cross validation, t he predictive faculty of the present model was as good as that of back propagation. The experiential rule by experts for time series data of sensory evaluation could be more sufficiently explained by the presen t model than by back propagation. The main advantage of this model was that its predictive faculty was compensated by Bayesian probabilities .