In response to the frequent counterfeiting of Wuchang rice in the market, an effective method to identify brand rice is proposed. Taking the near-infrared spectroscopy data of a total of 373 grains of rice from the four origins (Wuchang, Shangzhi, Yanshou, and Fangzheng) as the observations, kernelprincipal component analysis(KPCA) was employed to reduce the dimensionality, and Fisher discriminant analysis(FDA) and k-nearest neighbor algorithm (KNN) were used to identify brand rice respectively. The effects of the two recognition methods are very good, and that of KNN is relatively better. Howerver the shortcomings of KNN are obvious. For instance, it has only one test dimension and its test of samples is not delicate enough. In order to further improve the recognition accuracy, fuzzy k-nearest neighbor set is defined and fuzzy probability theory is employed to get a new recognition method –Two-Parameter KNN discrimination method. Compared with KNN algorithm, this method increases the examination dimension. It not only examines the proportion of the number of samples in each pattern class in the k-nearest neighbor set, but also examines the degree of similarity between the center of each pattern class and the sample to be identified. Therefore, the recognition process is more delicate and the recognition accuracy is higher. In the identification of brand rice, the discriminant accuracy of Two-Parameter KNN algorithm is significantly higher than that of FDA and that of KNN algorithm.

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二参数KNN算法及其在品牌大米识别中的应用

针对市场上五常大米假冒现象频发的问题，提出了一种有效的大米品牌识别方法。以4个产地（武昌、尚植、延寿、方正）共373粒大米的近红外光谱数据为观测资料，采用核主成分分析（KPCA）降维，Fisher判别分析(FDA) 和 k-近邻算法 (KNN) 分别用于识别品牌大米。两种识别方法的效果都很好，KNN的效果相对较好。但是KNN的缺点也很明显。比如它只有一个测试维度，对样本的测试不够精细。为了进一步提高识别准确率，定义了模糊k-最近邻集，并利用模糊概率理论得到了一种新的识别方法——双参数KNN判别法。与KNN算法相比，该方法增加了检查维度。它不仅考察k-近邻集中每个模式类的样本数所占的比例，还考察每个模式类的中心与待识别样本的相似程度。因此，识别过程更加细腻，识别准确率更高。在品牌大米的识别中，双参数KNN算法的判别准确率明显高于FDA和KNN算法。这种方法增加了检查维度。它不仅考察k-近邻集中每个模式类的样本数所占的比例，还考察每个模式类的中心与待识别样本的相似程度。因此，识别过程更加细腻，识别准确率更高。在品牌大米的识别中，双参数KNN算法的判别准确率明显高于FDA和KNN算法。这种方法增加了检查维度。它不仅考察k-近邻集中每个模式类的样本数所占的比例，还考察每个模式类的中心与待识别样本的相似程度。因此，识别过程更加细腻，识别准确率更高。在品牌大米的识别中，双参数KNN算法的判别准确率明显高于FDA和KNN算法。识别过程更细腻，识别准确率更高。在品牌大米的识别中，双参数KNN算法的判别准确率明显高于FDA和KNN算法。识别过程更细腻，识别准确率更高。在品牌大米的识别中，双参数KNN算法的判别准确率明显高于FDA和KNN算法。