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基于改进模糊聚类的烟草品质集成评价模型
引用本文: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.
作者姓名:尹梅  周国雄
作者单位:1. 湖南工程职业技术学院信息工程系,湖南 长沙,410151;2. 中南林业科技大学计算机与信息工程学院,湖南长沙,410004
基金项目:国家自然科学基金项目(60975049)
摘    要:针对烟草化学成分与烟草品质之间难以建立确定的数学模型的问题,提出了一种基于改进模糊聚类的烟草品质评价方法。该方法以烟叶样品的化学成分的差异性为依据,以模型分类结果与专家评吸结果的一致性为目标,利用模拟退火算法对现有的模糊聚类算法进行优化改进,建立基分类器;在此基础上,利用Ada Boost将基分类器对于不同样本集的多个分类结果进行集成,形成最终的烟草品质评价模型。以130组烟叶作为烟草样本,测定了各烟叶样品中总糖、还原糖、总氮、烟碱、氧化钾、氯离子、蛋白质7种化学成分含量,并采用改进的模糊聚类方法与神经网络算法、模糊聚类算法进行对比试验,该方法的误检率为6.7%,具有提升小样本数据的辨识能力,优于所比较的其他2种方法。

关 键 词:模糊聚类  模拟退火  专家评吸  烟草品质评价

An improved fuzzy clustering-based ensemble evaluation model for tobacco quality
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.
Keywords:fuzzy clustering  simulated annealing  expert evaluation  tobacco quality evaluation
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