Effect of solar dynamics parameters on the formation of substorm activity
An algorithm for retrieving the AL index dynamics from the parameters of solar-wind plasma and the interplanetary magnetic field (IMF) has been developed. Along with other geoeffective parameters of the solar wind, an integral parameter in the form of the cumulative sum Σ[N* V 2 ] is used to determi...
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| Veröffentlicht in: | Geomagnetism and Aeronomy 2017-05, Vol.57 (3), p.251-256 |
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| creator | Barkhatov, N. A. Vorob’ev, V. G. Revunov, S. E. Yagodkina, O. I. |
| description | An algorithm for retrieving the
AL
index dynamics from the parameters of solar-wind plasma and the interplanetary magnetic field (IMF) has been developed. Along with other geoeffective parameters of the solar wind, an integral parameter in the form of the cumulative sum Σ[N*
V
2
] is used to determine the process of substorm formation. The algorithm is incorporated into a framework developed to retrieve the
AL
index of an Elman-type artificial neural network (ANN) containing an additional layer of neurons that provides an “internal memory” of the prehistory of the dynamical process to be retrieved. The ANN is trained on data of 70 eight-hour-long time intervals, including the periods of isolated magnetospheric substorms. The efficiency of this approach is demonstrated by numerical neural-network experiments on retrieving the dynamics of the
AL
index from the of solar wind and IMF parameters during substorms. |
| doi_str_mv | 10.1134/S0016793217030021 |
| format | Article |
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AL
index dynamics from the parameters of solar-wind plasma and the interplanetary magnetic field (IMF) has been developed. Along with other geoeffective parameters of the solar wind, an integral parameter in the form of the cumulative sum Σ[N*
V
2
] is used to determine the process of substorm formation. The algorithm is incorporated into a framework developed to retrieve the
AL
index of an Elman-type artificial neural network (ANN) containing an additional layer of neurons that provides an “internal memory” of the prehistory of the dynamical process to be retrieved. The ANN is trained on data of 70 eight-hour-long time intervals, including the periods of isolated magnetospheric substorms. The efficiency of this approach is demonstrated by numerical neural-network experiments on retrieving the dynamics of the
AL
index from the of solar wind and IMF parameters during substorms.</description><identifier>ISSN: 0016-7932</identifier><identifier>EISSN: 1555-645X</identifier><identifier>EISSN: 0016-7940</identifier><identifier>DOI: 10.1134/S0016793217030021</identifier><language>eng</language><publisher>Moscow: Pleiades Publishing</publisher><subject>Algorithms ; Artificial neural networks ; Dynamics ; Earth and Environmental Science ; Earth Sciences ; Efficiency ; Geophysics/Geodesy ; Interplanetary magnetic field ; Learning theory ; Magnetic fields ; Magnetospheres ; Magnetospheric substorms ; Neural networks ; Neurons ; Solar activity ; Solar flares ; Solar wind ; Storms</subject><ispartof>Geomagnetism and Aeronomy, 2017-05, Vol.57 (3), p.251-256</ispartof><rights>Pleiades Publishing, Ltd. 2017</rights><rights>Geomagnetism and Aeronomy is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-553c63870d4f23d205d9ac5616c2bc41530af7fc71b94b53189fa6c290d7fce93</citedby><cites>FETCH-LOGICAL-c316t-553c63870d4f23d205d9ac5616c2bc41530af7fc71b94b53189fa6c290d7fce93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1134/S0016793217030021$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1134/S0016793217030021$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Barkhatov, N. A.</creatorcontrib><creatorcontrib>Vorob’ev, V. G.</creatorcontrib><creatorcontrib>Revunov, S. E.</creatorcontrib><creatorcontrib>Yagodkina, O. I.</creatorcontrib><title>Effect of solar dynamics parameters on the formation of substorm activity</title><title>Geomagnetism and Aeronomy</title><addtitle>Geomagn. Aeron</addtitle><description>An algorithm for retrieving the
AL
index dynamics from the parameters of solar-wind plasma and the interplanetary magnetic field (IMF) has been developed. Along with other geoeffective parameters of the solar wind, an integral parameter in the form of the cumulative sum Σ[N*
V
2
] is used to determine the process of substorm formation. The algorithm is incorporated into a framework developed to retrieve the
AL
index of an Elman-type artificial neural network (ANN) containing an additional layer of neurons that provides an “internal memory” of the prehistory of the dynamical process to be retrieved. The ANN is trained on data of 70 eight-hour-long time intervals, including the periods of isolated magnetospheric substorms. The efficiency of this approach is demonstrated by numerical neural-network experiments on retrieving the dynamics of the
AL
index from the of solar wind and IMF parameters during substorms.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Dynamics</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Efficiency</subject><subject>Geophysics/Geodesy</subject><subject>Interplanetary magnetic field</subject><subject>Learning theory</subject><subject>Magnetic fields</subject><subject>Magnetospheres</subject><subject>Magnetospheric substorms</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Solar activity</subject><subject>Solar flares</subject><subject>Solar wind</subject><subject>Storms</subject><issn>0016-7932</issn><issn>1555-645X</issn><issn>0016-7940</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kEFLxDAQhYMoWFd_gLeA5-pM06Sboyyru7DgQQVvJU0T7bJtapIV9t-bsh4E8TTMvO-9gUfINcItIivvngFQVJIVWAEDKPCEZMg5z0XJ305JNsn5pJ-TixC2kCDOMSPrpbVGR-osDW6nPG0Pg-o7HeiovOpNND5QN9D4Yah1vlexS9tE75sQ04EqHbuvLh4uyZlVu2CufuaMvD4sXxarfPP0uF7cb3LNUMScc6YFm1fQlrZgbQG8lUpzgUIXjS6RM1C2srrCRpYNZziXViVNQpuuRrIZuTnmjt597k2I9dbt_ZBe1ihBIqIQmCg8Utq7ELyx9ei7XvlDjVBPjdV_Gkue4ugJiR3ejf-V_K_pGy_XbII</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Barkhatov, N. 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I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-553c63870d4f23d205d9ac5616c2bc41530af7fc71b94b53189fa6c290d7fce93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Dynamics</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Efficiency</topic><topic>Geophysics/Geodesy</topic><topic>Interplanetary magnetic field</topic><topic>Learning theory</topic><topic>Magnetic fields</topic><topic>Magnetospheres</topic><topic>Magnetospheric substorms</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Solar activity</topic><topic>Solar flares</topic><topic>Solar wind</topic><topic>Storms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barkhatov, N. A.</creatorcontrib><creatorcontrib>Vorob’ev, V. 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A.</au><au>Vorob’ev, V. G.</au><au>Revunov, S. E.</au><au>Yagodkina, O. I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of solar dynamics parameters on the formation of substorm activity</atitle><jtitle>Geomagnetism and Aeronomy</jtitle><stitle>Geomagn. Aeron</stitle><date>2017-05-01</date><risdate>2017</risdate><volume>57</volume><issue>3</issue><spage>251</spage><epage>256</epage><pages>251-256</pages><issn>0016-7932</issn><eissn>1555-645X</eissn><eissn>0016-7940</eissn><abstract>An algorithm for retrieving the
AL
index dynamics from the parameters of solar-wind plasma and the interplanetary magnetic field (IMF) has been developed. Along with other geoeffective parameters of the solar wind, an integral parameter in the form of the cumulative sum Σ[N*
V
2
] is used to determine the process of substorm formation. The algorithm is incorporated into a framework developed to retrieve the
AL
index of an Elman-type artificial neural network (ANN) containing an additional layer of neurons that provides an “internal memory” of the prehistory of the dynamical process to be retrieved. The ANN is trained on data of 70 eight-hour-long time intervals, including the periods of isolated magnetospheric substorms. The efficiency of this approach is demonstrated by numerical neural-network experiments on retrieving the dynamics of the
AL
index from the of solar wind and IMF parameters during substorms.</abstract><cop>Moscow</cop><pub>Pleiades Publishing</pub><doi>10.1134/S0016793217030021</doi><tpages>6</tpages></addata></record> |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Algorithms Artificial neural networks Dynamics Earth and Environmental Science Earth Sciences Efficiency Geophysics/Geodesy Interplanetary magnetic field Learning theory Magnetic fields Magnetospheres Magnetospheric substorms Neural networks Neurons Solar activity Solar flares Solar wind Storms |
| title | Effect of solar dynamics parameters on the formation of substorm activity |
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