Modeling of thermodynamic properties of substances by neural networks
A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H/sub 2/O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iter...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3932 vol.6 |
|---|---|
| container_issue | |
| container_start_page | 3927 |
| container_title | |
| container_volume | 6 |
| creator | Lilja, R. Hamalainen, J.J. |
| description | A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H/sub 2/O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iterative algorithm. Large tables characteristic of previous interpolation methods are not needed. The neural network models enable new process simulation applications. |
| doi_str_mv | 10.1109/IJCNN.1999.830784 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_830784</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>830784</ieee_id><sourcerecordid>830784</sourcerecordid><originalsourceid>FETCH-ieee_primary_8307843</originalsourceid><addsrcrecordid>eNp9jssOgjAUBRsfic8P0FV_ALwVCnRtNGqiK_emwlWrQEkLMfy9Nbp2NZkzm0PIjIHPGIjFbr86Hn0mhPCTAOIk7JAh4zzxAgHLLhm5CQLOlyLquQAi8WIeRwMysvYBEEEciiFZH3SGuSpvVF9pfUdT6KwtZaFSWhldoakV2k-zzcXWskydXVpaYmNk7lC_tHnaCelfZW5x-uOYzDfr02rrKUQ8V0YV0rTn78vgb3wDvOk-eA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Modeling of thermodynamic properties of substances by neural networks</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Lilja, R. ; Hamalainen, J.J.</creator><creatorcontrib>Lilja, R. ; Hamalainen, J.J.</creatorcontrib><description>A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H/sub 2/O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iterative algorithm. Large tables characteristic of previous interpolation methods are not needed. The neural network models enable new process simulation applications.</description><identifier>ISSN: 1098-7576</identifier><identifier>ISBN: 0780355296</identifier><identifier>ISBN: 9780780355293</identifier><identifier>EISSN: 1558-3902</identifier><identifier>DOI: 10.1109/IJCNN.1999.830784</identifier><language>eng</language><publisher>IEEE</publisher><subject>Analytical models ; Automation ; Function approximation ; Interpolation ; Iterative algorithms ; Neural networks ; Numerical simulation ; Temperature ; Testing ; Thermodynamics</subject><ispartof>IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999, Vol.6, p.3927-3932 vol.6</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/830784$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/830784$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Lilja, R.</creatorcontrib><creatorcontrib>Hamalainen, J.J.</creatorcontrib><title>Modeling of thermodynamic properties of substances by neural networks</title><title>IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)</title><addtitle>IJCNN</addtitle><description>A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H/sub 2/O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iterative algorithm. Large tables characteristic of previous interpolation methods are not needed. The neural network models enable new process simulation applications.</description><subject>Analytical models</subject><subject>Automation</subject><subject>Function approximation</subject><subject>Interpolation</subject><subject>Iterative algorithms</subject><subject>Neural networks</subject><subject>Numerical simulation</subject><subject>Temperature</subject><subject>Testing</subject><subject>Thermodynamics</subject><issn>1098-7576</issn><issn>1558-3902</issn><isbn>0780355296</isbn><isbn>9780780355293</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jssOgjAUBRsfic8P0FV_ALwVCnRtNGqiK_emwlWrQEkLMfy9Nbp2NZkzm0PIjIHPGIjFbr86Hn0mhPCTAOIk7JAh4zzxAgHLLhm5CQLOlyLquQAi8WIeRwMysvYBEEEciiFZH3SGuSpvVF9pfUdT6KwtZaFSWhldoakV2k-zzcXWskydXVpaYmNk7lC_tHnaCelfZW5x-uOYzDfr02rrKUQ8V0YV0rTn78vgb3wDvOk-eA</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Lilja, R.</creator><creator>Hamalainen, J.J.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1999</creationdate><title>Modeling of thermodynamic properties of substances by neural networks</title><author>Lilja, R. ; Hamalainen, J.J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_8307843</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Analytical models</topic><topic>Automation</topic><topic>Function approximation</topic><topic>Interpolation</topic><topic>Iterative algorithms</topic><topic>Neural networks</topic><topic>Numerical simulation</topic><topic>Temperature</topic><topic>Testing</topic><topic>Thermodynamics</topic><toplevel>online_resources</toplevel><creatorcontrib>Lilja, R.</creatorcontrib><creatorcontrib>Hamalainen, J.J.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lilja, R.</au><au>Hamalainen, J.J.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modeling of thermodynamic properties of substances by neural networks</atitle><btitle>IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)</btitle><stitle>IJCNN</stitle><date>1999</date><risdate>1999</risdate><volume>6</volume><spage>3927</spage><epage>3932 vol.6</epage><pages>3927-3932 vol.6</pages><issn>1098-7576</issn><eissn>1558-3902</eissn><isbn>0780355296</isbn><isbn>9780780355293</isbn><abstract>A new method based on neural networks was developed for the modeling of thermodynamic properties of substances. When applied to the mixture of air and H/sub 2/O, the preset accuracy of 1% was obtained at every test point and the neural networks proved to be 5000 times faster than a conventional iterative algorithm. Large tables characteristic of previous interpolation methods are not needed. The neural network models enable new process simulation applications.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.1999.830784</doi></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1098-7576 |
| ispartof | IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 1999, Vol.6, p.3927-3932 vol.6 |
| issn | 1098-7576 1558-3902 |
| language | eng |
| recordid | cdi_ieee_primary_830784 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Analytical models Automation Function approximation Interpolation Iterative algorithms Neural networks Numerical simulation Temperature Testing Thermodynamics |
| title | Modeling of thermodynamic properties of substances by neural networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-30T08%3A30%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Modeling%20of%20thermodynamic%20properties%20of%20substances%20by%20neural%20networks&rft.btitle=IJCNN'99.%20International%20Joint%20Conference%20on%20Neural%20Networks.%20Proceedings%20(Cat.%20No.99CH36339)&rft.au=Lilja,%20R.&rft.date=1999&rft.volume=6&rft.spage=3927&rft.epage=3932%20vol.6&rft.pages=3927-3932%20vol.6&rft.issn=1098-7576&rft.eissn=1558-3902&rft.isbn=0780355296&rft.isbn_list=9780780355293&rft_id=info:doi/10.1109/IJCNN.1999.830784&rft_dat=%3Cieee_6IE%3E830784%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=830784&rfr_iscdi=true |