期刊论文

Identificationon Rice Varieties Based on K-means Clustering Algorithm and BP Neural Network

Liu, C.

中文

中国粮油学报

1003-0174

2018

33

2

128-131 and 141

10.3969/j.issn.1003-0174.2018.02.021

61201452:国家自然科学基金 B2015001:湖北省教育厅科学技术研究项目

本校为第一且通讯机构

BP神经网络是利用误差逆向传播训练的前馈网络,具有自适应、实时学习的特点,在分类中广泛应用。但当样本类别过多,BP神经网络的分类精度显著降低。基于此,本研究提出了一种K-means聚类算法和BP神经网络相结合的方法作稻米品种鉴别。利用图像处理方法提取出11种稻米样品的灰度平均值、长宽比和圆形度三项特征参数,利用K-means聚类算法对所得数据进行聚类,聚类的结果作为BP神经网络的输入,训练得到分类器。实验结果表明,这种算法的分类效果要优于单一使用BP神经网络和K-means算法,且分类准确率达到80%,可见本实验的方法用于稻米品种鉴别是可行的。

BP neural network is characterized by adaptability and real-time learning,so it is used widely in classification.The more complex samples classify,the lower the accuracy of BP neural network is.So a method was purposed to identify rice varieties that combined K-means clustering algorithm with BP neural network.The gray-scale mean value,aspect ratio and circularity,which were the three parameters of the rice,were extracted by image processing.The K-means clustering algorithm was used to classify the data based on the fore-mentioned three paramet...