湖南农业大学学报（自然科学版）2016，Vol.42Issue(4)：359-364,6.
基于改进模糊聚类的烟草品质集成评价模型
An improved fuzzy clustering-based ensemble evaluation model for tobacco quality
摘要
Abstract
To solve the difficulty in establishing the mathematical model of the cigarette chemical composition and tobacco quality, an improved fuzzy clustering-based ensemble evaluation model for tobacco quality is proposed. The method first determined the differences in chemical components among tobacco samples, and to obtain consistency results between model classification and expert evaluation results, simulated annealing algorithm was used to optimize the existing fuzzy clustering algorithm, and base classifier was established. On this basis, multiple classification results for different sample sets by the classifiers were integrated using the AdaBoost, and the final tobacco quality evaluation models was formed. The contents of 7 kinds of chemical composition including total sugar, reducing sugar, total nitrogen, nicotine, potassium ion, chlorine ion and protein in 130 group of tobacco leaf were determined, contrast experiment is done by the improved fuzzy clustering method, neural network algorithm and fuzzy clustering algorithm, the results showed that the error detection rate of the improved fuzzy clustering method is 6.7%, indicating the improved method has the ability to recognize small sample data, and is superior to the other compared methods.关键词
模糊聚类/模拟退火/专家评吸/烟草品质评价Key words
fuzzy clustering/simulated annealing/expert evaluation/tobacco quality evaluation分类
信息技术与安全科学引用本文复制引用
尹梅,周国雄..基于改进模糊聚类的烟草品质集成评价模型[J].湖南农业大学学报（自然科学版）,2016,42(4):359-364,6.基金项目
国家自然科学基金项目(60975049) （60975049）
