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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.

History

Start Page

1

End Page

6

Number of Pages

6

Start Date

01/01/2012

Finish Date

01/01/2012

ISBN-13

9781467314909

Location

Brisbane, Qld., Australia

Publisher

IEEE Computational Intelligence Society

Place of Publication

New York

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); TBA Research Institute;

Era Eligible

Yes

Name of Conference

IEEE World Congress on Computational Intelligence;IEEE International Joint Conference on Neural Networks

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