论文标题
在质子 - 蛋白质碰撞中，人工神经网络和自适应神经模糊的推理系统之间的比率
Comparison between Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System For The Baryon-to-Meson Ratios in Proton-Proton Collisions
论文作者
论文摘要
本文介绍了两个系统，可以模拟和预测在高能质子 - 普罗顿（PP）碰撞中产生的颗粒比，这是横向动量和质量能量的函数。自适应神经模糊的推理系统（ANFI）和人工神经网络（ANN）系统是讨论的系统。通过训练数据点评估的训练颗粒比率的ANFIS和ANN模拟结果表明，与实验数据相匹配。 ANFIS和ANN的预测能力还使用未包含在培训中的数据点进行了测试，并且表现良好。结果清楚地表明，这些方法能够提取碰撞信息，并且它们很有帮助。同样，将ANFIS和ANN的结果与其他理论结果（Pythia（CR模式），Herwig7，Pythia，Pythia8（Monash）和Epos-LHC进行了比较。发现ANFI表现出更好的性能，并且比ANN系统和其他理论模型更快地接受了训练。
This article presents two systems that can simulate and predict Particles ratios created in high energy proton-proton (pp) collisions as a function of transverse momentum and the center-of-mass energy. An adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) system are the systems in discussion. The ANFIS and ANN simulation results for training particles ratios as evaluated with training data points revealed an excellent match to the experimental data. The ANFIS and ANN's prediction abilities were also tested using data points that were not included in training and they performed well. The results clearly show that these methods are capable of extracting collision information and that they are helpful. Also, ANFIS and ANN results were compared with additional theoretical results (PYTHIA (CR Mode), HERWIG7, PYTHIA, PYTHIA8 (Monash), and EPOS-LHC). It is found that the ANFIS shows better performance and is trained more quickly than the ANN system and other theoretical models.
