A neural networks-based fitting to high energy stopping power data for heavy ions in solid matter

Neural networks provide an alternative approach for the solution of complex non-linear data fitting problems. In this paper, we propose a novel technique using a multilayer perceptron neural network to fit high energy stopping power data, where the unknown stopping power functional form was fitted to experimental data by a set of linear combination of neurons. The projectiles of Li, B, N, O, Ne and P in the solid matters C, Si, Ti and Ni are illustrated as examples of the application. Using the resilient backpropagation algorithm, it can obtain more accurate fitting coefficients than conventional iterative methods. Our simulations show that a simple, accurate predictor based on neural network fitting can produce reliable predictions of stopping power values either at the energy position or for the projectile-target combination where no measured data currently exist.