基于BP神经网络的转筒式称量系统的称量效率预测  被引量:1

Weighing Efficiency Prediction in Rotor Weighing System Based on BP Neural Network

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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)

机构地区:[1]中国兵器装备集团自动化研究所有限公司智能制造事业部

出  处:《兵工自动化》2020年第2期63-65,77,共4页Ordnance Industry Automation

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.

关 键 词:BP神经网络 ANN预测模型 称量效率 预测精度 

分 类 号:TP15[自动化与计算机技术—控制理论与控制工程]

 

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