基于粗糙集和神经网络的三七质量评价

Tie Wang, Zhiguang Chen, Gaonan Wang, Jianyang Lin
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引用次数: 0

摘要

为了实时鉴别三七药材的质量,根据三七指纹打印机的特点,简要介绍了粗糙集的基本概念。由于粗糙集只能处理离散数据,数据的离散化是粗糙集应用于质量评价的关键因素,结合粗糙集和人工神经网络的特点,提出了一种基于聚类分类的离散化方法,其泛化效果良好。采用粗糙集和人工神经网络的方法对三七进行评估,无需任何额外的先验模型假设,粗糙集数据分析可以消除属性及其值的冗余,识别属性中的依赖关系。从样本数据中得到了化学模式分类系统的集体生产规律。根据这些规则建立的化学模式分类模型在化学领域的意义很容易理解,模型的预测效果也很好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Radix Notoginseng Quality Assessment Based on Rough Sets and Neutral Network
To discriminate the quality on traditional Chinese medicines RADIX NOTOGINSENG real-time, according to the characters of radix notoginseng finger printer, the basic concepts of rough set are introduced briefly. For rough sets can only deal with discrete data, the discretization of data is the key factor in the rough sets applied in quality assessment, we present a method of discretization based on cluster category which combined with the characteristic of rough sets and artificial neutral network, its generalization is well. Using methods rough sets and artificial neutral network to assess radix notoginseng without any additional prior model assumption, rough sets data analysis can eliminate the redundancy of attributes and its value, identify the dependence in the attributes. We get a collective production rules about the chemical pattern classification system from sample data. When the model of chemical pattern classification is built by these rules, its meaning is very understandable in chemical domain, and the prediction of the model is also well.
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