流量计量中BP神经网络温度补偿算法研究

摘要

In view of the nonlinear problem caused by temperature effect existing in using time difference method to measure flow rate, the temperature compensation algorithm based on BP neural network is proposed. This algorithm improves the prediction performance of BP neural network by introducing momentum factor and improving data sensitivity,and compensates flow measurement by establishing nonlinear mapping relationship between temperature and flow. Simulation analysis shows that the algorithm features better capability of data fusion and prediction. Furthermore,experimental verification indicates that the compensation performance of this algorithm is more stable than that of existing table lookup correction algorithm,the maximum error is within ± 2. 0% and the maximum absolute error variance is 0. 48,reaches grade II level. So the compensation algorithm has good value of engineering application.%针对时差法计量流量时受温度影响而存在的非线性问题,提出了基于BP神经网络的温度补偿算法。该算法通过引入动量因子和改善数据敏感度,提高了BP神经网络的预测能力,通过建立温度与流量之间的非线性映射关系来补偿流量计量。仿真分析可知,该算法表现出较好的数据融合及预测能力。实验验证进一步表明,相对于现有查表修正算法,该算法补偿性能稳定,最大误差在±2.0%以内,最大绝对误差方差为0.48,达到2级表水平,具有重要的工程应用价值。