Modeling of soft sensor based on artificial neural network for galactic cosmic rays application

Wayan Suparta, W. S. Putro

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

For successful designing of space radiation Galactic Cosmic Rays (GCRs) model, we develop a soft sensor based on the Artificial Neural Network (ANN) model. At the first step, the soft sensor based ANN was constructed as an alternative to model space radiation environment. The structure of ANN in this model is using Multilayer Perceptron (MLP) and Levenberg Marquardt algorithms with 3 inputs and 2 outputs. In the input variable, we use 12 years data (Corr, Uncorr and Press) of GCR particles obtained from Neutron Monitor of Bartol University (Fort Smith area) and the target output is (Corr and Press) from the same source but for Inuvik area in the Polar Regions. In the validation step, we obtained the Root Mean Square Error (RMSE) value of Corr 3.8670e-004 and Press 1.3414e-004 and Variance Accounted For (VAF) of Corr 99.9839 % and Press 99.9831% during the training section. After all the results obtained, then we applied into a Matlab GUI simulation (soft sensor simulation). This simulation will display the estimation of output value from input (Corr and Press). Testing results showed an error of 0.133% and 0.014% for Corr and Press, respectively.

Original languageEnglish
Article number012021
JournalJournal of Physics: Conference Series
Volume539
Issue number1
DOIs
Publication statusPublished - 2014

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sensors
extraterrestrial radiation
output
self organizing systems
graphical user interface
simulation
root-mean-square errors
polar regions
monitors
education
neutrons

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Modeling of soft sensor based on artificial neural network for galactic cosmic rays application. / Suparta, Wayan; Putro, W. S.

In: Journal of Physics: Conference Series, Vol. 539, No. 1, 012021, 2014.

Research output: Contribution to journalArticle

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