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An improved RBF neural network approach to nonlinear curve fitting

conference contribution
posted on 06.12.2017, 00:00 by Brijesh VermaBrijesh Verma, Minmei LiMinmei Li
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 : proceedings

Start Page

262

End Page

275

Number of Pages

14

Start Date

01/01/2015

Finish Date

01/01/2015

ISBN-13

9783319192215

Location

Palma de Mallorca, Spain

Publisher

Springer International Publishing

Place of Publication

Switzerland

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent Systems (2015- ); School of Engineering and Technology (2013- );

Era Eligible

Yes

Name of Conference

International Work-Conference on Artificial Neural Networks

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