In this paper, a new response approximation model for quadrant detector is proposed based on a BP Neural Network by employing different training algorithms. A total of 1001 data points are gathered to train and test the proposed network. Through optimal configuration, the network with 1 hidden layer and 8 hidden neurons with Log-sigmoid transfer functions in the hidden layer is determined to have the optimum performance. Furthermore, Levenberg-Marquardt (LM) is the best train algorithm while in this case the model is more precise than others. Besides, it shows a good ability to suppress the non-uniformity. The results of experiment reveal that the root mean square error using this model is about 1/5 of that using Fusion method when the beam radius is ${0.75} \textit {mm} $ . Meanwhile, its maximum errors under different radii are all less than ${6}\times {10}^{\text {-3}} \textit {mm} $ . Therefore, the new model would have a good application prospect in beam position measurements.

中文翻译：

---

使用优化的BP神经网络的象限检测器的新响应近似模型

在本文中，通过采用不同的训练算法，基于BP神经网络提出了一种新的象限检测器响应近似模型。总共收集了 1001 个数据点来训练和测试提议的网络。通过优化配置，确定具有 1 个隐藏层和 8 个隐藏神经元的网络在隐藏层中具有 Log-sigmoid 传递函数，具有最佳性能。此外，Levenberg-Marquardt (LM) 是最好的训练算法，而在这种情况下，模型比其他模型更精确。此外，它显示出良好的抑制不均匀性的能力。实验结果表明，当光束半径为 ${0.75} \textit {mm} $ . 同时，其在不同半径下的最大误差均小于 ${6}\times {10}^{\text {-3}} \textit {mm} $ . 因此，新模型在波束位置测量中具有良好的应用前景。