Application of Neural Networks in Chain Curve Modelling
A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chain...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 112 |
|---|---|
| container_issue | |
| container_start_page | 104 |
| container_title | |
| container_volume | |
| creator | Piegat, Andrzej Rejer, Izabela Mikolajczyk, Marek |
| description | A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chains. Obviously, as it was proven in numerous applications, always better results can be obtained when the modelling method corresponds to the system nature. Therefore, when a data distribution of an unknown system has a chain characteristic, the system should be also modelled with a chain, not a surface, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system. |
| doi_str_mv | 10.1007/11785231_12 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_19992726</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>19992726</sourcerecordid><originalsourceid>FETCH-LOGICAL-p219t-46b0c04bb37339b526bc3ade91e00910902ca9136f8e616a7299a71c4cf66283</originalsourceid><addsrcrecordid>eNpNkL1PwzAUxM2XRCmd-AeyMDAE3vNz7LyxqsqHVGDpHjmuU0JDEtktiP-eVO3ADXfD73TDCXGDcI8A5gHR5JkkLFCeiAmbnDIFlJkM9KkYoUZMiRSfiasjUDmdixEQyJSNoksxifETBhFqUvlImGnfN7Wz27prk65K3vwu2GaI7U8XNjGp22T2Yfe-C98-ee1Wvmnqdn0tLirbRD855lgsH-fL2XO6eH96mU0XaS-Rt6nSJThQZUmGiMtM6tKRXXlGD8AIDNJZRtJV7jVqaySzNeiUq7SWOY3F7WG2t9HZpgq2dXUs-lB_2fBbIDNLI_XQuzv04oDatQ9F2XWbWCAU--OKf8fRH60SWGQ</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Application of Neural Networks in Chain Curve Modelling</title><source>Springer Books</source><creator>Piegat, Andrzej ; Rejer, Izabela ; Mikolajczyk, Marek</creator><contributor>Tadeusiewicz, Ryszard ; Żurada, Jacek M. ; Rutkowski, Leszek ; Zadeh, Lotfi A.</contributor><creatorcontrib>Piegat, Andrzej ; Rejer, Izabela ; Mikolajczyk, Marek ; Tadeusiewicz, Ryszard ; Żurada, Jacek M. ; Rutkowski, Leszek ; Zadeh, Lotfi A.</creatorcontrib><description>A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chains. Obviously, as it was proven in numerous applications, always better results can be obtained when the modelling method corresponds to the system nature. Therefore, when a data distribution of an unknown system has a chain characteristic, the system should be also modelled with a chain, not a surface, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 3540357483</identifier><identifier>ISBN: 9783540357483</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540357506</identifier><identifier>EISBN: 3540357505</identifier><identifier>DOI: 10.1007/11785231_12</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Theoretical computing</subject><ispartof>Artificial Intelligence and Soft Computing – ICAISC 2006, 2006, p.104-112</ispartof><rights>Springer-Verlag Berlin Heidelberg 2006</rights><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/11785231_12$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/11785231_12$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>310,311,780,781,785,790,791,794,4051,4052,27930,38260,41447,42516</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19992726$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Tadeusiewicz, Ryszard</contributor><contributor>Żurada, Jacek M.</contributor><contributor>Rutkowski, Leszek</contributor><contributor>Zadeh, Lotfi A.</contributor><creatorcontrib>Piegat, Andrzej</creatorcontrib><creatorcontrib>Rejer, Izabela</creatorcontrib><creatorcontrib>Mikolajczyk, Marek</creatorcontrib><title>Application of Neural Networks in Chain Curve Modelling</title><title>Artificial Intelligence and Soft Computing – ICAISC 2006</title><description>A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chains. Obviously, as it was proven in numerous applications, always better results can be obtained when the modelling method corresponds to the system nature. Therefore, when a data distribution of an unknown system has a chain characteristic, the system should be also modelled with a chain, not a surface, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540357483</isbn><isbn>9783540357483</isbn><isbn>9783540357506</isbn><isbn>3540357505</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkL1PwzAUxM2XRCmd-AeyMDAE3vNz7LyxqsqHVGDpHjmuU0JDEtktiP-eVO3ADXfD73TDCXGDcI8A5gHR5JkkLFCeiAmbnDIFlJkM9KkYoUZMiRSfiasjUDmdixEQyJSNoksxifETBhFqUvlImGnfN7Wz27prk65K3vwu2GaI7U8XNjGp22T2Yfe-C98-ee1Wvmnqdn0tLirbRD855lgsH-fL2XO6eH96mU0XaS-Rt6nSJThQZUmGiMtM6tKRXXlGD8AIDNJZRtJV7jVqaySzNeiUq7SWOY3F7WG2t9HZpgq2dXUs-lB_2fBbIDNLI_XQuzv04oDatQ9F2XWbWCAU--OKf8fRH60SWGQ</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Piegat, Andrzej</creator><creator>Rejer, Izabela</creator><creator>Mikolajczyk, Marek</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Application of Neural Networks in Chain Curve Modelling</title><author>Piegat, Andrzej ; Rejer, Izabela ; Mikolajczyk, Marek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-46b0c04bb37339b526bc3ade91e00910902ca9136f8e616a7299a71c4cf66283</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Piegat, Andrzej</creatorcontrib><creatorcontrib>Rejer, Izabela</creatorcontrib><creatorcontrib>Mikolajczyk, Marek</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Piegat, Andrzej</au><au>Rejer, Izabela</au><au>Mikolajczyk, Marek</au><au>Tadeusiewicz, Ryszard</au><au>Żurada, Jacek M.</au><au>Rutkowski, Leszek</au><au>Zadeh, Lotfi A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Application of Neural Networks in Chain Curve Modelling</atitle><btitle>Artificial Intelligence and Soft Computing – ICAISC 2006</btitle><date>2006</date><risdate>2006</risdate><spage>104</spage><epage>112</epage><pages>104-112</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540357483</isbn><isbn>9783540357483</isbn><eisbn>9783540357506</eisbn><eisbn>3540357505</eisbn><abstract>A modelling process of an unknown multi-dimensional system is mostly performed with methods which describe the system by a multi-dimensional surface (e.g. neural networks (NNs)). Some systems, however, does not have a surface nature. On the contrary – their behavior resembles multi-dimensional chains. Obviously, as it was proven in numerous applications, always better results can be obtained when the modelling method corresponds to the system nature. Therefore, when a data distribution of an unknown system has a chain characteristic, the system should be also modelled with a chain, not a surface, method. The aim of this article is to present the alternative approach to the modelling process, in which the multi-dimensional model of an unknown system is built on the basis of a set of two-dimensional NNs instead of one multi-dimensional NN. The proposed approach results in a chain multi-dimensional model of an analyzed system.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11785231_12</doi><tpages>9</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Artificial Intelligence and Soft Computing – ICAISC 2006, 2006, p.104-112 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19992726 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Theoretical computing |
| title | Application of Neural Networks in Chain Curve Modelling |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T11%3A06%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Application%20of%20Neural%20Networks%20in%20Chain%20Curve%20Modelling&rft.btitle=Artificial%20Intelligence%20and%20Soft%20Computing%20%E2%80%93%20ICAISC%202006&rft.au=Piegat,%20Andrzej&rft.date=2006&rft.spage=104&rft.epage=112&rft.pages=104-112&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=3540357483&rft.isbn_list=9783540357483&rft_id=info:doi/10.1007/11785231_12&rft_dat=%3Cpascalfrancis_sprin%3E19992726%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540357506&rft.eisbn_list=3540357505&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |