GA-BP神经网络对片烟结构的预测研究

针对烟叶复烤厂打叶过程中片烟结构难以预测的问题,文中提出了一种基于MATLAB图像处理的GA-BP神经网络预测模型。对于烟叶分类问题,基于获取的烟叶图片,利用MATLAB软件对图片做预处理,提取衡量片烟结构的主要特征变量,并利用行业标准与聚类分析算法对数据进行分类。通过统计学的标准数学方法,构建了遗传算法优化的BP神经网络预测模型对主要影响参数进行预测优化。研究结果表明,文中所提方法预测精度较高,预测极差均小于0.059,可有效解决打叶过程中片烟的预测问题。

A GA-BP Neural Network for Predicting the Structure of Leaf Tobacco

In view of the problem that it is difficult to predict the structure of tobacco during the threshing process of redrying plant, a GA-BP neural network prediction model based on MATLAB image processing is proposed. For the classification of tobacco leaf, the obtained tobacco leaf images are preprocessed using of the MATLAB software. Then, the main characteristic variables that measure the structure of the tobacco are extracted, and industry standards and cluster analysis algorithms are used to classify the data. Through the standard mathematical method of statistics, the BP neural network prediction model optimized by genetic algorithm is constructed to predict and optimize the main influencing parameters. The research results show that the method proposed in this study has high prediction accuracy, and the prediction range is less than 0.059, which indicates that the method can effectively solve the problem of prediction of slices of tobacco in the process of threshing.