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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Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM)
2022
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| 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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| 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. |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uitm.ir-84049 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2022 |
| publisher | Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) |
| record_format | eprints |
| 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 |
| 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.] |
| title | 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_short | 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.] |
| topic | Neural networks (Computer science) Geomagnetism |
| url | https://ir.uitm.edu.my/id/eprint/84049/1/84049.pdf https://ir.uitm.edu.my/id/eprint/84049/ |
| url_provider | http://ir.uitm.edu.my/ |