Training sample dimensions impact on artificial neural network optimal structure
The paper addresses the problem of electric load forecasting, using artificial neural networks mathematical apparatus, subject to error minimization on the long forecasting interval. Balanced artificial neural network architecture gives the possibility to maintain small deviation between forecasted...
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creator | Manusov, V. Z. Makarov, I. S. Dmitriev, S. A. Eroshenko, S. A. |
description | The paper addresses the problem of electric load forecasting, using artificial neural networks mathematical apparatus, subject to error minimization on the long forecasting interval. Balanced artificial neural network architecture gives the possibility to maintain small deviation between forecasted and real values simultaneously with constrained squared error variation maintenance. Proposed methodology was verified using real data. |
doi_str_mv | 10.1109/EEEIC.2013.6549608 |
format | Conference Proceeding |
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Z.</creatorcontrib><creatorcontrib>Makarov, I. S.</creatorcontrib><creatorcontrib>Dmitriev, S. A.</creatorcontrib><creatorcontrib>Eroshenko, S. A.</creatorcontrib><title>Training sample dimensions impact on artificial neural network optimal structure</title><title>2013 12th International Conference on Environment and Electrical Engineering</title><addtitle>EEEIC</addtitle><description>The paper addresses the problem of electric load forecasting, using artificial neural networks mathematical apparatus, subject to error minimization on the long forecasting interval. Balanced artificial neural network architecture gives the possibility to maintain small deviation between forecasted and real values simultaneously with constrained squared error variation maintenance. Proposed methodology was verified using real data.</description><subject>artificial neural network</subject><subject>Artificial neural networks</subject><subject>Biological neural networks</subject><subject>Energy consumption</subject><subject>Forecasting</subject><subject>neural network training</subject><subject>Training</subject><subject>Vectors</subject><isbn>1467330604</isbn><isbn>9781467330602</isbn><isbn>9781467330589</isbn><isbn>1467330582</isbn><isbn>9781467330596</isbn><isbn>1467330590</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkMFOhDAURWuMiTryA7rpD4DvUSjt0hB0JplEF-N6UkoxVSikLTH-vURZnZzFvbm5hNwjZIggH5umOdRZDsgyXhaSg7ggiawEFrxiDEohL8ntJhyKa5KE8AkAa5hLiTfk7eSVddZ90KDGeTC0s6NxwU4uUDvOSkc6Oap8tL3VVg3UmcX_IX5P_otOc7Tj6iH6RcfFmzty1ashmGTjjrw_N6d6nx5fXw710zG1WJUxNQWXbQuCS-ywwhWKM40glG5FyzrGNJNc55KXADmKDrE3XBmJLBeoDNuRh_9ea4w5z35d4X_O2wnsFyhGUM8</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Manusov, V. Z.</creator><creator>Makarov, I. S.</creator><creator>Dmitriev, S. A.</creator><creator>Eroshenko, S. A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201305</creationdate><title>Training sample dimensions impact on artificial neural network optimal structure</title><author>Manusov, V. Z. ; Makarov, I. S. ; Dmitriev, S. A. ; Eroshenko, S. A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-e469bb08691d171691a63c108acb8b3d33c396c296500218d11fe6ae913281ae3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>artificial neural network</topic><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>Energy consumption</topic><topic>Forecasting</topic><topic>neural network training</topic><topic>Training</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Manusov, V. Z.</creatorcontrib><creatorcontrib>Makarov, I. S.</creatorcontrib><creatorcontrib>Dmitriev, S. A.</creatorcontrib><creatorcontrib>Eroshenko, S. A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Manusov, V. Z.</au><au>Makarov, I. S.</au><au>Dmitriev, S. A.</au><au>Eroshenko, S. A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Training sample dimensions impact on artificial neural network optimal structure</atitle><btitle>2013 12th International Conference on Environment and Electrical Engineering</btitle><stitle>EEEIC</stitle><date>2013-05</date><risdate>2013</risdate><spage>156</spage><epage>159</epage><pages>156-159</pages><isbn>1467330604</isbn><isbn>9781467330602</isbn><eisbn>9781467330589</eisbn><eisbn>1467330582</eisbn><eisbn>9781467330596</eisbn><eisbn>1467330590</eisbn><abstract>The paper addresses the problem of electric load forecasting, using artificial neural networks mathematical apparatus, subject to error minimization on the long forecasting interval. Balanced artificial neural network architecture gives the possibility to maintain small deviation between forecasted and real values simultaneously with constrained squared error variation maintenance. Proposed methodology was verified using real data.</abstract><pub>IEEE</pub><doi>10.1109/EEEIC.2013.6549608</doi><tpages>4</tpages></addata></record> |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | artificial neural network Artificial neural networks Biological neural networks Energy consumption Forecasting neural network training Training Vectors |
title | Training sample dimensions impact on artificial neural network optimal structure |
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