发表机构
National Institute of Science Education and Research; Sri Sri University（国家科学教育与研究学院; 斯里斯里大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用人工神经网络作为互补数据驱动模型，基于切线电流计测量数据预测地球磁场水平分量，通过优化输入变量（含tan(theta)和线圈磁场）实现了高精度预测，验证了ANN在本科物理实验数据分析中的有效性。
AI 中文摘要
切线电流计（TG）是一种标准本科实验室实验，通过测量磁针偏转角与流过圆形线圈的电流之间的关系来估算地球磁场的水平分量（BH）。在本研究中，人工神经网络（ANN）被用作一种互补的数据驱动模型来预测BH的值。使用由50匝和500匝线圈获得的225个观测值组成的数据集来开发ANN模型。经过质量控制后，保留了223个观测值，并将其划分为训练集（70%）、验证集（15%）和测试集（15%）。该模型使用具有Tanh激活函数的前馈神经网络进行优化。比较了三种具有不同输入变量的模型（模型A、B和C）以获得最佳性能。模型C使用了五个输入变量：电流、偏转角、tan(theta)、线圈产生的磁场和匝数。加入tan(theta)显著提高了预测性能，而进一步加入线圈产生的磁场则进一步提升了性能。模型C给出了最佳测试性能，R2 = 0.99053，RMSE = 0.54076微特斯拉，MAE = 0.32940微特斯拉。实验和ANN预测的BH值还与采用的当地地磁参考值39.0微特斯拉进行了比较。实验平均值和ANN预测平均值分别为37.38898微特斯拉和37.32161微特斯拉。结果表明，ANN作为分析本科物理实验室实验中实验变异性和非线性关系的互补工具是有用的。
英文摘要
The Tangent Galvanometer (TG) is a standard undergraduate laboratory experiment for estimating the horizontal component of Earth's magnetic field (BH) by measuring the angle of deflection of a magnetic needle corresponding to the current flowing through a circular coil. In this study, an artificial neural network (ANN) is used as a complementary data-driven model to predict the value of BH. A dataset comprising 225 observations obtained using 50-turn and 500-turn coils was used for developing the ANN model. After quality control, 223 observations were retained and divided into training (70%), validation (15%), and testing (15%) subsets. The model was optimized using a feed-forward neural network with Tanh activation. Three different models (Models A, B, and C) with different input variables were compared for optimum performance. Model C used five input variables: current, deflection angle, tan(theta), magnetic field produced by the coil, and number of turns. The addition of tan(theta) produced a substantial improvement in prediction performance, which was further improved by including the magnetic field produced by the coil. Model C gave the best test performance, with R2 = 0.99053, RMSE = 0.54076 microT, and MAE = 0.32940 microT. The experimental and ANN-predicted values of BH were also compared with an adopted local geomagnetic reference value of 39.0 microT. The mean experimental and ANN-predicted values were 37.38898 microT and 37.32161 microT, respectively. The results demonstrate the usefulness of ANN as a complementary tool for analyzing experimental variability and nonlinear relationships in an undergraduate physics laboratory experiment.
Comments11 pages, 8 figures
