中文摘要：

<正>The Monte-Carlo samples of pion,kaon and proton generated from 0.3 GeV/c to 1.2 GeV/c by the ’tester’ generator from SIMBES which are used to simulate the detector of BESⅡare identified with the Bayesian neural networks(BNN).The pion identification and misidentification efficiencies are obviously better at high momentum region using BNN than the methods of X~2 analysis of dE/dX and TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3 GeV/c to 1.2 GeV/c using BNN than the methods of x~2 analysis.The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of x~2 analysis.The anti-proton identification and misidentification efficiencies are better below 0.6 GeV/c using BNN than the methods of X~2 analysis.

英文摘要：

The Monte-Carlo samples of pion, kaon and proton generated from 0.3 GeV/c to 1.2 GeV/c by the ‘tester＇ generator from SIMBES which are used to simulate the detector of BES Ⅱ are identified with the Bayesian neural networks （BNN）. The pion identification and misidentification efficiencies are obviously better at high momentum region using BNN than the methods of χ^2 analysis of dE/dX and TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3 GeV/c to 1.2 GeV/c using BNN than the methods of X2 analysis. The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of χ^2 analysis. The anti-proton identification and misidentification efficiencies are better below 0.6 GeV/c using BNN than the methods of χ^2 analysis.