基于PCA-K-means和PCA-SOM神经网络的葡萄酒分类  

A Study on the Application of PCA-K-means and PSO-SOM to the Classification of Grape Wines

作　　者：霍双红 胡红萍[1] 白艳萍[1] 王建中[1] 

机构地区：[1]中北大学理学院,山西太原030051

出　　处：《数学的实践与认识》2016年第17期168-173,共6页Mathematics in Practice and Theory

基　　金：国家自然科学基金(61275120)

摘　　要：针对葡萄酒物理和化学数据成分冗余,提出了两种葡萄酒分类的算法,分别是主成分分析K均值和主成分分析自组织神经网络算法.这两种算法对葡萄酒的物理化学成分进行了主成分分析,提取了主要的影响因素,将输入维数降低,再利用K均值和自组织神经网络算法分别对葡萄酒进行分类和比较.实验结果表明,PCA-K-means和PCA-SOM都具有较高的准确率,都有一定的使用价值和可操作性,并且PCA-K-means算法优于其它的算法.As the data of physical and chemical components rape winesare Characterized by redundancy,this paper proposes two models based on PCA-K-means and PCAself-organizing Neural networks for the classification of grape wines.First,it analyzes the principal physical and chemical components of grape wines and the major influencing factors in order to reduce input dimensions;second,using K-means and self-organizing neural network algorithm to compare the effect of wine classification.The result indicates that the PCA-K-means model has higher precision than the models PCA-self-organizing Neural networks.

关 键 词：主成分分析 K-平均算法 自组织神经网络 

分 类 号：TS262.6[轻工技术与工程—发酵工程] TP183[轻工技术与工程—食品科学与工程]