G-LMBPNN: A New Fashion Color Prediction Model
Since the current fashion color forecasts have some disadvantages in practical application, there is considerable interest in building models that can predict fashion value of the colors precisely and swiftly from historical data. This paper proposed a new forecasting model called G-LMBPNN (Gray Lev...
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| creator | Wu Ye-zhe Sun Li Le Jia-jin |
| description | Since the current fashion color forecasts have some disadvantages in practical application, there is considerable interest in building models that can predict fashion value of the colors precisely and swiftly from historical data. This paper proposed a new forecasting model called G-LMBPNN (Gray Levenberg-Marquardt Back Propagation Neural Network). It utilizes gray process to obscure the data sequence and learns the nonlinear relation through optimized BP neural network training. Finally, we whiten the simulation sequence to get the predicted value. We show the effectiveness of G-LMBPNN through a comprehensive experimental evaluation based on three models. |
| doi_str_mv | 10.1109/CASoN.2010.118 |
| format | Conference Proceeding |
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This paper proposed a new forecasting model called G-LMBPNN (Gray Levenberg-Marquardt Back Propagation Neural Network). It utilizes gray process to obscure the data sequence and learns the nonlinear relation through optimized BP neural network training. Finally, we whiten the simulation sequence to get the predicted value. 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We show the effectiveness of G-LMBPNN through a comprehensive experimental evaluation based on three models.</description><subject>Artificial neural networks</subject><subject>Biological system modeling</subject><subject>BP neural network</subject><subject>data mining</subject><subject>Data models</subject><subject>fashion color prediction</subject><subject>G-LMBPNN model</subject><subject>gray theory</subject><subject>Image color analysis</subject><subject>Neurons</subject><subject>Predictive models</subject><subject>Training</subject><isbn>9781424487851</isbn><isbn>1424487854</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjEtLw0AURgdEqNRsu3EzfyBx7szcebiLwbZCGgt2X5KZG4xER5KC-O-tj29zOGfxMbYCUQAIf1uVz6kppPh1d8Eybx1oqbWzDmHBsnl-FedplFLBFSs2eb273zfNHS95Q5983c4vQ3rnVRrTxPcTxSGcfsIuRRqv2WXfjjNl_1yyw_rhUG3z-mnzWJV1PnhxykHHEDUBCgzBUecFChOj9T6CxM4C9kraaMM5G4Mtogo9OkktgQXbqSW7-bsdiOj4MQ1v7fR1RKOMB6e-Aaq1Pow</recordid><startdate>201009</startdate><enddate>201009</enddate><creator>Wu Ye-zhe</creator><creator>Sun Li</creator><creator>Le Jia-jin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201009</creationdate><title>G-LMBPNN: A New Fashion Color Prediction Model</title><author>Wu Ye-zhe ; Sun Li ; Le Jia-jin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-14dcd4e1505cc8eb90506dd799d125b715f327d7c506665a553cf582eae1717b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Artificial neural networks</topic><topic>Biological system modeling</topic><topic>BP neural network</topic><topic>data mining</topic><topic>Data models</topic><topic>fashion color prediction</topic><topic>G-LMBPNN model</topic><topic>gray theory</topic><topic>Image color analysis</topic><topic>Neurons</topic><topic>Predictive models</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Wu Ye-zhe</creatorcontrib><creatorcontrib>Sun Li</creatorcontrib><creatorcontrib>Le Jia-jin</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>Wu Ye-zhe</au><au>Sun Li</au><au>Le Jia-jin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>G-LMBPNN: A New Fashion Color Prediction Model</atitle><btitle>2010 International Conference on Computational Aspects of Social Networks</btitle><stitle>cason</stitle><date>2010-09</date><risdate>2010</risdate><spage>501</spage><epage>504</epage><pages>501-504</pages><isbn>9781424487851</isbn><isbn>1424487854</isbn><abstract>Since the current fashion color forecasts have some disadvantages in practical application, there is considerable interest in building models that can predict fashion value of the colors precisely and swiftly from historical data. This paper proposed a new forecasting model called G-LMBPNN (Gray Levenberg-Marquardt Back Propagation Neural Network). It utilizes gray process to obscure the data sequence and learns the nonlinear relation through optimized BP neural network training. Finally, we whiten the simulation sequence to get the predicted value. We show the effectiveness of G-LMBPNN through a comprehensive experimental evaluation based on three models.</abstract><pub>IEEE</pub><doi>10.1109/CASoN.2010.118</doi><tpages>4</tpages></addata></record> |
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| subjects | Artificial neural networks Biological system modeling BP neural network data mining Data models fashion color prediction G-LMBPNN model gray theory Image color analysis Neurons Predictive models Training |
| title | G-LMBPNN: A New Fashion Color Prediction Model |
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