安徽农业科学2019，Vol.47Issue(2)：197-201,5.DOI:10.3969/j.issn.0517-6611.2019.02.061
基于BP神经网络的智能定量供种系统设计
Design of Intelligent Quantitative Seeding System Based on BP Neural Network
摘要
Abstract
In order to realize the continuous seeding of duble-vibrating precision seed meter under the premise of uniform filling, the intelligent quantitative supply seed system was designed.In order to improve the precision of quantitative supply seed, a prediction model of quantitative supply seed was established based on BP neural network for the spoon-type outer groove wheel seeding device.After sample data preprocessing and network initialization, a neural network model with hidden node number 6 was established, and then BP network training was performed.The results showed that when the network model training step reached 71 steps, the mean square error of the network was 4.61×10-5, less than the set value of 5×10-5, which met the requirements.For the established network model, a total of 16 test samples were tested in 4 groups.The results showed that the relative error of the predicted values based on the BP neural network prediction model was smaller, and the accuracy was higher than the theoretical model, and the relative error of the neural network was less than 5％, the obtained square error of the sample error was 5.59×10-4, which was less than the set target value of 8×10-4, which satisfied the preset requirement.Finally, using the established quantitative seeding prediction model, four different 1 000-grain super rice seeds were simulated to obtain the relationship between the seed wheel rotation speed and the supply seed quantity with amplitudes of 0, 5, 10 and 15 μm.The research results can provide a basis for determining the working parameters of the quantitative seeder.关键词
BP神经网络/定量供种/建模/仿真Key words
BP neural network/Quantitative seed supply/Modeling/Simulation分类
农业科技引用本文复制引用
梁秋艳,潘小莉,仇志锋,周海波..基于BP神经网络的智能定量供种系统设计[J].安徽农业科学,2019,47(2):197-201,5.基金项目
黑龙江省高校科技成果产业化前期研发培育项目(1253CGZH06) （1253CGZH06）
