EVALUATION OF WINES RATING USING
NEURAL NETWORKS in Matlab
In this works, 9 characteristics were used as inputs, in Backpropagation Neural Network, to evaluate the rating of wines as output. Also wines’ rating was evaluated by using significant words that seem to have descriptive meanings for the quality, by creating the appropriate code in order to transform the string text of sensory description to corresponding numbers and finally all the text description of the wines, transformed in ASCII characters, in order to evaluate the rating of wines by Backpropagation. The use of genetic algorithms was examined in order to improve the results. This work will be very useful because it will permit the automatic and more objective evaluation of wines’ rating.
NEURAL NETWORKS in Matlab
In this works, 9 characteristics were used as inputs, in Backpropagation Neural Network, to evaluate the rating of wines as output. Also wines’ rating was evaluated by using significant words that seem to have descriptive meanings for the quality, by creating the appropriate code in order to transform the string text of sensory description to corresponding numbers and finally all the text description of the wines, transformed in ASCII characters, in order to evaluate the rating of wines by Backpropagation. The use of genetic algorithms was examined in order to improve the results. This work will be very useful because it will permit the automatic and more objective evaluation of wines’ rating.