LAUSR

Wine, an alcoholic beverage made from fermented grapes, has become an increasingly popular drink. However, wine regions may directly affect the quality and taste of the wine, and misjudgment of the wine regions leads to confusion for dealers and consumers in choosing wine types. In recent decades, different methods in machine learning have been presented and investigated the pattern classification. In this paper, based on the existing results, a modified multi-output Chebyshev-polynomial feed-forward neural network (MOCPFFNN) is presented, analyzed, and applied to the pattern classification of wines regions. According to the orthogonal polynomial theory, the activation functions of the MOCPFFNN are improved to some Chebyshev polynomials. In addition, to have a lower computational complexity, the presented neural network model is automatically determined by the eight-fold cross validation (8FCV) algorithm and the weight direct determination (WDD) algorithm. Finally, comparisons are made among the presented model and other classical methods, e.g., feed-forward back propagation (FFBP), layer recurrent neural network (LRNN), and nonlinear auto regressive with exogenous inputs (NARX), K-nearest neighbors (KNN), random forest, which confirm that the modified MOCPFFNN has the best approximation and generalization performance in the pattern classification of wine regions, with the accuracy rates of the training set and test set reaching 99.17% and 94.83%, respectively. Moreover, the variance of the accuracy of the presented MOCPFFNN method in the experiments is 0, which illustrates its high robustness in pattern classification.