Comparison of the BP training algorithm and LVQ neural networks for e, μ, π identification

Two different kinds of neural networks, feed-forward multi-layer mode with Back-Propagation training algorithm (BP) and Kohonenʹs Learning Vector Quantization networks (LVQ), are adopted for the identification of e, μ, π particles in Beijing Spectrometer (BES) experiment. The data samples for training and test consist of μ from cosmic ray, e and π from experimental data by strict selection. Although their momentum spectra are non-uniform, the identification efficiencies given by BP are quite uniform versus momentum, and LVQ is little worse. At least in this application BP is shown to be more powerful in pattern recognition than LVQ.