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Compensation of automatic weighing error of belt weigher based on BP neural network

机译：基于BP神经网络的皮带秤自动称重误差补偿

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摘要

A belt weigher is widely used in industrial production and trade settlement; however, it is difficult to maintain its nominal measuring accuracy in service. With the belt weigher indication, average speed of belt, variation in belt sag, running deviation of belt, environmental temperature, and humidity as inputs, and the control instrument indication as output, a BP neural network model to compensate the automatic weighing error of the belt weigher is built. We obtained the sample data depending on the test system for the type evaluation of the belt weigher in the Jiangsu Institute of Metrology, as well as supplemental relevant parameters for the monitoring devices. The BP model is trained and validated using MATLAB. The validation results show that the absolute value of the maximum relative automatic weighing error output by the BP model is less than 0.5%, significantly lower than the error before using the BP model for compensation. The BP model is effective, feasible, and practical for compensating the automatic weighing error of the belt weigher.
机译：皮带秤广泛用于工业生产和贸易沉降;但是，难以维持其在服务中的标称测量精度。随着皮带秤指示，皮带平均速度，皮带凹陷的变化，皮带，环境温度和湿度的偏差为输入，以及控制仪器指示作为输出，一个BP神经网络模型补偿了自动称重误差建造带秤。我们根据江苏计量研究所的皮带秤类类型评估的测试系统获得了样本数据，以及监控设备的补充相关参数。使用MATLAB培训并验证BP模型。验证结果表明，BP模型输出的最大相对自动称重误差的绝对值小于0.5％，显着低于使用BP模型进行补偿之前的误差。 BP模型是有效的，可行的，实用，可用于补偿皮带秤的自动称重误差。

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