【摘要】 研究了两种不同的神经元网络法，ＢＰ网络和ＬＶＱ网络，对北京谱仪（ＢＥＳ）实验中ｅ，μ，π粒子的鉴别，取得了较常规方法要好的结果．用于训练和检验的μ子样本来自宇宙线事例，ｅ和π粒子则是由真实实验数据精选的，虽然样本本身具有非均匀的动量谱，但ＢＰ网络的检验结果给出的粒子选择效率在整个动量区间却仍然具有相当均匀的分布，ＬＶＱ网络稍逊之．至少是在我们研究的这一课题中证明了ＢＰ网络的模式识别功能要优于ＬＶＱ网络更多还原

【Abstract】 Two different kinds of neural network methods,BP and LVQ ,are applied to the identification of e,μ,π particles in BES experiment ,and better results are obtained.The μ data samples used for training and tsets are from cosmic events,e and π are strictly selected from real experimental data by very tight cuts.Although their momentum spectrums are non uniform,interseting enough is that selection efficiencies given by BP tset results are quite uniform in the whole momentum range.The results from LVQ is little worse.It shows that BP is more powerful in pattern recognition than LVQ at least in this study.更多还原