标题： 通过人工神经网络恢复饱和$γ$信号波形 显示英文标题

标题： Recovery of Saturated $γ$ Signal Waveforms by Artificial Neural Networks

摘要： 粒子有时可能具有超出辐射检测硬件范围的能量，导致信号饱和并丢失有用信息。 因此，我们研究了使用人工神经网络（ANN）来恢复$\gamma$信号的饱和波形的可能性。 测试了几种 ANN，即反向传播（BP）、简单循环（Elman）、径向基函数（RBF）和广义径向基函数（GRBF）神经网络，并与基于 Marrone 模型的拟合方法进行了比较。 发现 GBRFNN 表现最佳。 显示英文摘要

摘要： Particle may sometimes have energy outside the range of radiation detection hardware so that the signal is saturated and useful information is lost. We have therefore investigated the possibility of using an Artificial Neural Network (ANN) to restore the saturated waveforms of $\gamma$ signals. Several ANNs were tested, namely the Back Propagation (BP), Simple Recurrent (Elman), Radical Basis Function (RBF) and Generalized Radial Basis Function (GRBF) neural networks (NNs) and compared with the fitting method based on the Marrone model. The GBRFNN was found to perform best.