基于PCA神经网络的葡萄酒评价模型研究

Research of the wine evaluation model based on PCA & neural network

利用主成分分析与RBF神经网络相结合,建立葡萄酒质量评价预报模型,并进行训练和仿真验证。该模型运用SPSS软件对葡萄酒中影响风味指标进行主成分分析,将多变量、非线性的原始数据进行降维,保留原始信息的主要信息,把原来若干个属性变量综合成几个不相关主成分分量;再以计算结果作为RBF网络的输入数据,葡萄酒的感官评价得分作为网络的输出数据,建立葡萄酒主要理化指标与葡萄酒质量的关系模型。结果表明:该评价模型的建立,缩短了葡萄酒评价的周期,克服了品酒师聚集的困难;与传统RBF网络相比,大大简化了网络结构,提高了网络的训练速度和预报精度,为质量评价问题提供了一种的研究思路。

Using the principal component analysis combined with RBF neural network,to set up the wine quality forecast model,meanwhile,training and simulating it. By using the SPSS software,realize principal component analysis on the wine flavor indexes,which reducing the multivariable and nonlinear dimension of primary data,retaining the original information of the main information,associating with the original several attribute variables to be synthesized into a few principal components. The calculation results are regarded as input of RBF network,the wine sensory evaluation score as the output of the network,to set up the main physical and chemical indicators with the wine quality relationship model. The results show that,the evaluation model has shorten the wine evaluation period,overcome the difficulty of wine taster gathering. Compared with the traditional RBF network,simplify the network structure,improve the network training speed and prediction accuracy,at the same time,provide a research way for quality evaluation.