Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model
To smooth the randomness, a grey forecasting model is formulated using the data of accumulating generation operator (AGO) rather than original data. Then the inverse accumulating generation operator (IAGO) is applied to find the predicted values of original data. It is proved that the errors from IA...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2015-02, Vol.19 (2), p.483-488 |
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| creator | Wu, Lifeng Liu, Sifeng Yao, Ligen Xu, Ruiting Lei, Xunping |
| description | To smooth the randomness, a grey forecasting model is formulated using the data of accumulating generation operator (AGO) rather than original data. Then the inverse accumulating generation operator (IAGO) is applied to find the predicted values of original data. It is proved that the errors from IAGO are affected by the order number of AGO. To achieve an accurate prediction, GM(2,1), which stands for one-variable and second-order differential equation, has been improved by means of fractional order AGO. Finally, four real data sets are imported for comparing the performance of the developed GM(2,1) with several other grey models, such as traditional GM(2,1) and GM(1,1). The simulation results show that optimized GM(2,1) has higher performances not only on model fitting but also on forecasting. |
| doi_str_mv | 10.1007/s00500-014-1268-y |
| format | Article |
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Then the inverse accumulating generation operator (IAGO) is applied to find the predicted values of original data. It is proved that the errors from IAGO are affected by the order number of AGO. To achieve an accurate prediction, GM(2,1), which stands for one-variable and second-order differential equation, has been improved by means of fractional order AGO. Finally, four real data sets are imported for comparing the performance of the developed GM(2,1) with several other grey models, such as traditional GM(2,1) and GM(1,1). The simulation results show that optimized GM(2,1) has higher performances not only on model fitting but also on forecasting.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-014-1268-y</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accumulation ; Accuracy ; Artificial Intelligence ; Computational Intelligence ; Control ; Differential equations ; Engineering ; Errors ; Forecasting ; Mathematical Logic and Foundations ; Mathematical models ; Mechatronics ; Methodologies and Application ; Robotics</subject><ispartof>Soft computing (Berlin, Germany), 2015-02, Vol.19 (2), p.483-488</ispartof><rights>Springer-Verlag Berlin Heidelberg 2014</rights><rights>Springer-Verlag Berlin Heidelberg 2014.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-89134bcd4188bc913b5b6984e0590bb478934221d1d007f17bd68973c829d5f43</citedby><cites>FETCH-LOGICAL-c386t-89134bcd4188bc913b5b6984e0590bb478934221d1d007f17bd68973c829d5f43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-014-1268-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2917904823?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,21386,27922,27923,33742,41486,42555,43803,51317,64383,64387,72239</link.rule.ids></links><search><creatorcontrib>Wu, Lifeng</creatorcontrib><creatorcontrib>Liu, Sifeng</creatorcontrib><creatorcontrib>Yao, Ligen</creatorcontrib><creatorcontrib>Xu, Ruiting</creatorcontrib><creatorcontrib>Lei, Xunping</creatorcontrib><title>Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>To smooth the randomness, a grey forecasting model is formulated using the data of accumulating generation operator (AGO) rather than original data. 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The simulation results show that optimized GM(2,1) has higher performances not only on model fitting but also on forecasting.</description><subject>Accumulation</subject><subject>Accuracy</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Differential equations</subject><subject>Engineering</subject><subject>Errors</subject><subject>Forecasting</subject><subject>Mathematical Logic and Foundations</subject><subject>Mathematical models</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Robotics</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEtLxDAUhYsoOI7-AHcB19G82iRLGXzBgBtnHdokLR3aZrxphf57Uyu4cnUffOfAOVl2S8k9JUQ-REJyQjChAlNWKDyfZRsqOMdSSH3-szMsC8Evs6sYj4QwKnO-ycIhtkODaijt2Iah7FAA5wGV1k791JXLE40BgXeT9cgDBIgJDz1qhy8P0f-h3qHGDx6SKFmG07IFQKFGDfgZ9cH57jq7qMsu-pvfuc0Oz08fu1e8f3952z3useWqGLHSlIvKOkGVqmw6qrwqtBKe5JpUlZBKc8EYddSl9DWVlSuUltwqpl1eC77N7lbfE4TPycfRHMMEKV80TFOpiVCMJ4qulIUQI_janKDtS5gNJWbp1ay9mtSrWXo1c9KwVRMTOzQe_pz_F30DwsV8hw</recordid><startdate>20150201</startdate><enddate>20150201</enddate><creator>Wu, Lifeng</creator><creator>Liu, Sifeng</creator><creator>Yao, Ligen</creator><creator>Xu, Ruiting</creator><creator>Lei, Xunping</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20150201</creationdate><title>Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model</title><author>Wu, Lifeng ; 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| subjects | Accumulation Accuracy Artificial Intelligence Computational Intelligence Control Differential equations Engineering Errors Forecasting Mathematical Logic and Foundations Mathematical models Mechatronics Methodologies and Application Robotics |
| title | Using fractional order accumulation to reduce errors from inverse accumulated generating operator of grey model |
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