摘要
为解决普通数理方法难以进行转筒式称量效率预测的问题,基于BP神经网络,建立人工神经网络算法模型。对模型输入项进行分析,找出影响称重效率的重点关联因素,研究在5种输入层因素下,模型称量效率的预测精度,并进行仿真分析。结果表明:该模型能有效预测转筒式称量方式的称量效率,且预测精度较高。
The artificial neural network(ANN)prediction model based on BP neural network is built to predict the weighing efficiency of rotor weighing machine which is difficult to carry out by ordinary mathematical methods.The key factors of the weighing efficiency are found by analysis of the input layers.The prediction accuracy of the weighing efficiency of the model is studied under 5 kinds of input layer factors,and the simulation analysis is carried out.The results show that the model can effectively predict the weighing efficiency of the rotary weighing method,and the prediction accuracy is high.
作者
许杰淋
何川
周丽娟
王雪晶
Xu Jielin;He Chuan;Zhou Lijuan;Wang Xuejing(Department of Intelligent Manufacture,Automation Research Institute Co.,Ltd.of China South Industries Group Corporation,Mianyang 621000,China)
出处
《兵工自动化》
2020年第2期63-65,77,共4页
Ordnance Industry Automation
关键词
BP神经网络
ANN预测模型
称量效率
预测精度
BP neural network
ANN prediction model
weighing efficiency
prediction accuracy
