On the Predictive Power of ANN Method for RMF Model Parameters


XVI. International Balkan Workshop on Applied Physics, 2016, Constanta, Romania, 7 - 09 July 2016, pp.122-123

  • Publication Type: Conference Paper / Full Text
  • City: Constanta
  • Country: Romania
  • Page Numbers: pp.122-123


Relativistic Mean Field (RMF) model is a powerful tool for predictions of various ground-state properties of nuclei such as binding energy, radii and deformation of nuclei. It is a phenomenological model and mainly its success aroused from fitting of some arameters of it from experimental data. Calculations of nuclear properties of nuclei within the framework of RMF model is achieved iteratively because of nature of this model. On the other hand, artificial neural network (ANN) method is successful in understanding the nonlinear relation data. Considering this point, we have obtained RMF predictions for binding energy of some spherical nuclei by changing of RMF model parameters step by step. Thus, we have obtained data of binding energies for different parameters values. Later we have employed ANN method for this data by using experimental binding energies for obtaining of best parameters for considered nuclei. Thus we have discussed
predictive power of ANN method for RMF model parameters.