Improving the Intelligent Prediction Model for Macro-economy
Identifieur interne : 001A04 ( Istex/Corpus ); précédent : 001A03; suivant : 001A05Improving the Intelligent Prediction Model for Macro-economy
Auteurs : Jianbo Fan ; Lidan Shou ; Jinxiang DongSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2006.
Abstract
Abstract: This paper presents a novel approach to macro-economy forecasting based on the Fuzzy Neural Networks. This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.
Url:
DOI: 10.1007/11816157_16
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This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Jianbo Fan</name><affiliations><json:string>College of Electron & Information Engineering, Ningbo University of Technology, 315016, Ningbo, Zhejiang</json:string><json:string>E-mail: jbfan@163.com</json:string></affiliations></json:item><json:item><name>Lidan Shou</name><affiliations><json:string>Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, Zhejiang</json:string><json:string>E-mail: should@cs.zju.edu.cn</json:string></affiliations></json:item><json:item><name>Jinxiang Dong</name><affiliations><json:string>Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, Zhejiang</json:string><json:string>E-mail: djx@cs.zju.edu.cn</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: This paper presents a novel approach to macro-economy forecasting based on the Fuzzy Neural Networks. 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This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I16013</label><label>I16021</label><label>I21017</label><label>I22021</label><label>I2203X</label><item><term>Computer Science</term></item><item><term>Computation by Abstract Devices</term></item><item><term>Algorithm Analysis and Problem Complexity</term></item><item><term>Artificial Intelligence (incl. 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ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-37273-8</BookID><BookTitle>Intelligent Computing</BookTitle><BookSubTitle>International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006. Proceedings, Part I</BookSubTitle><BookVolumeNumber>4113</BookVolumeNumber><BookSequenceNumber>4113</BookSequenceNumber><BookDOI>10.1007/11816157</BookDOI><BookTitleID>140756</BookTitleID><BookPrintISBN>978-3-540-37271-4</BookPrintISBN><BookElectronicISBN>978-3-540-37273-8</BookElectronicISBN><BookChapterCount>166</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16013" Priority="1" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I16021" Priority="2" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22021" Priority="4" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I2203X" Priority="5" Type="Secondary">Pattern Recognition</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>De-Shuang</GivenName><FamilyName>Huang</FamilyName></EditorName><Contact><Email>dshuang@iim.ac.cn</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Kang</GivenName><FamilyName>Li</FamilyName></EditorName><Contact><Email>kangli2@uiuc.edu</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>George</GivenName><GivenName>William</GivenName><FamilyName>Irwin</FamilyName></EditorName><Contact><Email>g.irwin@qub.ac.uk</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Institute of Intelligent Machines</OrgDivision><OrgName>Chinese Academy of Sciences</OrgName><OrgAddress><City>Hefei</City><State>Anhui</State><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>School of Electronics, Electrical Engineering and Computer Science</OrgDivision><OrgName>Queen’s University Belfast</OrgName><OrgAddress><Street>Stranmillis Road</Street><Postcode>BT9 5AH</Postcode><City>Belfast</City><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Neural Networks</PartTitle><PartChapterCount>62</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Intelligent Computing</BookTitle></PartContext></PartInfo><Chapter ID="Chap16" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>16</ChapterID><ChapterDOI>10.1007/11816157_16</ChapterDOI><ChapterSequenceNumber>16</ChapterSequenceNumber><ChapterTitle Language="En">Improving the Intelligent Prediction Model for Macro-economy</ChapterTitle><ChapterFirstPage>156</ChapterFirstPage><ChapterLastPage>167</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2006</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>1</PartID><BookID>978-3-540-37273-8</BookID><BookTitle>Intelligent Computing</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Jianbo</GivenName><FamilyName>Fan</FamilyName></AuthorName><Contact><Email>jbfan@163.com</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Lidan</GivenName><FamilyName>Shou</FamilyName></AuthorName><Contact><Email>should@cs.zju.edu.cn</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Jinxiang</GivenName><FamilyName>Dong</FamilyName></AuthorName><Contact><Email>djx@cs.zju.edu.cn</Email></Contact></Author><Affiliation ID="Aff19"><OrgDivision>College of Electron & Information Engineering</OrgDivision><OrgName>Ningbo University of Technology</OrgName><OrgAddress><State>Zhejiang</State><City>Ningbo</City><Postcode>315016</Postcode></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Institute of Artificial Intelligence</OrgDivision><OrgName>Zhejiang University</OrgName><OrgAddress><State>Zhejiang</State><City>Hangzhou</City><Postcode>310027</Postcode></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This paper presents a novel approach to macro-economy forecasting based on the Fuzzy Neural Networks. This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.</Para></Abstract><ArticleNote Type="Misc"><SimplePara>The author gratefully acknowledges that this work has been supported by the 2006 K.C. Wong Education Foundation.</SimplePara></ArticleNote></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Improving the Intelligent Prediction Model for Macro-economy</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Improving the Intelligent Prediction Model for Macro-economy</title></titleInfo><name type="personal"><namePart type="given">Jianbo</namePart><namePart type="family">Fan</namePart><affiliation>College of Electron & Information Engineering, Ningbo University of Technology, 315016, Ningbo, Zhejiang</affiliation><affiliation>E-mail: jbfan@163.com</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Lidan</namePart><namePart type="family">Shou</namePart><affiliation>Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, Zhejiang</affiliation><affiliation>E-mail: should@cs.zju.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jinxiang</namePart><namePart type="family">Dong</namePart><affiliation>Institute of Artificial Intelligence, Zhejiang University, 310027, Hangzhou, Zhejiang</affiliation><affiliation>E-mail: djx@cs.zju.edu.cn</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2006</dateIssued><copyrightDate encoding="w3cdtf">2006</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: This paper presents a novel approach to macro-economy forecasting based on the Fuzzy Neural Networks. This method employs the expert opinions, statistical analysis and the Genetic Algorithm, to enhance the model of Fuzzy Neural Network. Our method combines the expert opinions and the results of statistical analysis to determine the input parameters of the prediction model, and adopts the Genetic Algorithm to process the original sample data. We use the fuzzy logic system to establish a set of fuzzy rules and utilize an EBP (Error Back Propagation) algorithm to train the network and adjust the parameters of the membership functions. The experimental results of the system indicates that the method is efficient and robust, producing high-precision results. This method could be extended to other application areas.</abstract><relatedItem type="host"><titleInfo><title>Intelligent Computing</title><subTitle>International Conference on Intelligent Computing, ICIC 2006, Kunming, China, August 16-19, 2006. Proceedings, Part I</subTitle></titleInfo><name type="personal"><namePart type="given">De-Shuang</namePart><namePart type="family">Huang</namePart><affiliation>Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China</affiliation><affiliation>E-mail: dshuang@iim.ac.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Kang</namePart><namePart type="family">Li</namePart><affiliation>Carnegie Mellon University</affiliation><affiliation>E-mail: kangli2@uiuc.edu</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">George</namePart><namePart type="given">William</namePart><namePart type="family">Irwin</namePart><affiliation>School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Stranmillis Road, BT9 5AH, Belfast, UK</affiliation><affiliation>E-mail: g.irwin@qub.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2006</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic></subject><identifier type="DOI">10.1007/11816157</identifier><identifier type="ISBN">978-3-540-37271-4</identifier><identifier type="eISBN">978-3-540-37273-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">140756</identifier><identifier type="BookID">978-3-540-37273-8</identifier><identifier type="BookChapterCount">166</identifier><identifier type="BookVolumeNumber">4113</identifier><identifier type="BookSequenceNumber">4113</identifier><identifier type="PartChapterCount">62</identifier><part><date>2006</date><detail type="part"><title>Neural Networks</title></detail><detail type="volume"><number>4113</number><caption>vol.</caption></detail><extent 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type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>University of California, Los Angeles, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2006</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier 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