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An improved RBF neural network approach to nonlinear curve fitting
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.
History
Parent Title
Advances in computational intelligence : 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015 : proceedingsStart Page
262End Page
275Number of Pages
14Start Date
2015-01-01Finish Date
2015-01-01ISBN-13
9783319192215Location
Palma de Mallorca, SpainPublisher
Springer International PublishingPlace of Publication
SwitzerlandPublisher DOI
Full Text URL
Peer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Centre for Intelligent Systems (2015- ); School of Engineering and Technology (2013- );Era Eligible
- Yes