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.
出处
《数学的实践与认识》
北大核心
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)
关键词
主成分分析
K-平均算法
自组织神经网络
principal component analysis
PCA-K-means algorithm
self-organizing Neural networks.
