基于搜寻者-支持向量机的葡萄酒品质鉴别模型

摘要

针对葡萄酒品质鉴别问题，提出一种基于搜寻者-支持向量机（ SOA-SVM）葡萄酒品质鉴别模型。该模型以葡萄酒多维化学成分作为输入，以葡萄酒的品质类型作为输出，采用搜寻者优化算法对支持向量机模型惩罚因子和核函数参数进行优选，从而建立最优的SOA-SVM葡萄酒品质鉴别模型。应用该模型对UCI机器学习数据库中wine数据集进行实例分析，结果表明SOA算法收敛精度高、收敛速度快，且该模型能够取得优良的分类效果。%Aiming at the wine quality identification problems,a model based on SOA-SVM for identif-ying wine quality was put forward in the paper.The multi-dimensional chemical composition for the wine were taken as model input,the wine type was taken as the output,the SOA algorithm is used in the model for parameter optimization including penalty factor and the kernel function parameter,thereby the optimal SOA-SVM identification model can be established.In the paper,the model is used to analyze the wine set from the UCI machine learning database and the results show that SOA has a high convergence precision and a fast convergence rate.A good classification effect can be achieved through the SOA-SVM quality identification model.