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Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]

This paper discusses geomagnetic field attempt modelling using an Artificial Neural Network (ANN). The local horizontal component of geomagnetic field data was collected on April 2011 (equinox) during a solar quiet day at recent solar cycle inclination-24 using the Magnetic Data Acquisition System (...

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Main Authors: Hashim, M. H., Jusoh, M. H., Burhanudin, K., Yassin, I. M., Hamid, N. S. A., Radzi, Z. M., Yoshikawa, A.
Format: Article
Language:English
Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2022
Subjects:
Neural networks (Computer science)
Geomagnetism
Online Access:https://ir.uitm.edu.my/id/eprint/84049/1/84049.pdf
https://ir.uitm.edu.my/id/eprint/84049/
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spelling my.uitm.ir.840492023-09-15T07:43:11Z https://ir.uitm.edu.my/id/eprint/84049/ Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.] jmeche Hashim, M. H. Jusoh, M. H. Burhanudin, K. Yassin, I. M. Hamid, N. S. A. Radzi, Z. M. Yoshikawa, A. Neural networks (Computer science) Geomagnetism This paper discusses geomagnetic field attempt modelling using an Artificial Neural Network (ANN). The local horizontal component of geomagnetic field data was collected on April 2011 (equinox) during a solar quiet day at recent solar cycle inclination-24 using the Magnetic Data Acquisition System (MAGDAS) in Langkawi, Malaysia, in the low latitude region. The calculated average values (mean) of the H component geomagnetic field variation during Equinox 2011 characterised the dominant geomagnetic field during that particular solar cycle. The difference in amplitude of maximum and minimum values shows a regular diurnal variation of the geomagnetic field during Sq in the low latitude region. The output training utilised these calculated mean values during the modelling attempt. Meanwhile, the input training utilised proton density, solar wind plasma speed, plasma flow pressure, and Interplanetary Magnetic Field (IMF) space data using Non-Linear Auto Regressive Input (NARX). Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2022-11 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/84049/1/84049.pdf Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]. (2022) Journal of Mechanical Engineering (JMechE) <https://ir.uitm.edu.my/view/publication/Journal_of_Mechanical_Engineering_=28JMechE=29/>, 11 (1): 19. pp. 333-345. ISSN 1823-5514 ; 2550-164X
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Neural networks (Computer science)
Geomagnetism
spellingShingle Neural networks (Computer science)
Geomagnetism
Hashim, M. H.
Jusoh, M. H.
Burhanudin, K.
Yassin, I. M.
Hamid, N. S. A.
Radzi, Z. M.
Yoshikawa, A.
Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
description This paper discusses geomagnetic field attempt modelling using an Artificial Neural Network (ANN). The local horizontal component of geomagnetic field data was collected on April 2011 (equinox) during a solar quiet day at recent solar cycle inclination-24 using the Magnetic Data Acquisition System (MAGDAS) in Langkawi, Malaysia, in the low latitude region. The calculated average values (mean) of the H component geomagnetic field variation during Equinox 2011 characterised the dominant geomagnetic field during that particular solar cycle. The difference in amplitude of maximum and minimum values shows a regular diurnal variation of the geomagnetic field during Sq in the low latitude region. The output training utilised these calculated mean values during the modelling attempt. Meanwhile, the input training utilised proton density, solar wind plasma speed, plasma flow pressure, and Interplanetary Magnetic Field (IMF) space data using Non-Linear Auto Regressive Input (NARX).
format Article
author Hashim, M. H.
Jusoh, M. H.
Burhanudin, K.
Yassin, I. M.
Hamid, N. S. A.
Radzi, Z. M.
Yoshikawa, A.
author_facet Hashim, M. H.
Jusoh, M. H.
Burhanudin, K.
Yassin, I. M.
Hamid, N. S. A.
Radzi, Z. M.
Yoshikawa, A.
author_sort Hashim, M. H.
title Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
title_short Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
title_full Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
title_fullStr Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
title_full_unstemmed Preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with Exogeneous Input (NARX) / M. H. Hashim ... [et al.]
title_sort preliminary modelling of solar quiet geomagnetic field average using non-linear autoregressive with exogeneous input (narx) / m. h. hashim ... [et al.]
publisher Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
publishDate 2022
url https://ir.uitm.edu.my/id/eprint/84049/1/84049.pdf
https://ir.uitm.edu.my/id/eprint/84049/
_version_ 1778165989260656640
score 13.211869

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