Efficient Neuro-Fuzzy Inference System (ANFIS) and Neural
Networks Systems for Different Beams Collisions with Light
Background and Objectives: The Neuro-fuzzy Inference System (ANFIS) and a Neural Networks (NNets) system are two effective and famous systems. This study aimed to study the behavior of the multiplicity distribution of shower particles for some metals and predict the behavior for others. In addition to make a comparative comparison between the two proposed systems. Methodology: The ANFIS and NNets systems are trained and tested to simulate and predict the non-linear relationship for multiplicity distribution of shower particles produced from the P, 2H, 4He, 6Li, 7Li, 12C, 16O, 24Mg, 28Si and 32S with light (HCNO) emulsion at 4.5 AGev/c. Results: The simulation results from the ANFIS based model and NNets are compared with the corresponding experimented data for different beams collisions with light nuclei. Conclusion: The predicted values of the ANFIS and NNets are expected to be accurately as the experimental data. The ANFIS and NNets give the providing of extensive procedure in modeling of high-energy physics. However, the obtained results of ANFIS is better than the NNets in the test and predicted data.
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