Abstract
This article presents a new framework for fitting measured scientific data to a simple empirical formula by introducing an additional linear neuron to the standard Gaussian kernel radial basis function (RBF) neural networks. The proposed method is first used to evaluate two benchmark datasets (Preschool boy and titanium heat) and then is applied to fit a set of stopping power data (MeV energetic carbon projectiles in elemental target materials C, Al, Si, Ti, Ni, Cu, Ag and Au) from high energy physics experiments. Without increasing computational complexity, the proposed approach significantly improves accuracy of fitting. Based on this type RBF neural network, a simple 6-parameter empirical formula is developed for various potential applications in curve fitting and nonlinear regression problems.
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Li, M.M., Verma, B. (2015). An Improved RBF Neural Network Approach to Nonlinear Curve Fitting. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_22
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DOI: https://doi.org/10.1007/978-3-319-19222-2_22
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