Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization
Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis funct...
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 | 386 |
|---|---|
| container_issue | |
| container_start_page | 383 |
| container_title | |
| container_volume | 3 |
| creator | Wang, Ting Wang, Heng Xie, Hao-fei |
| description | Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network. |
| doi_str_mv | 10.1109/ICCAE.2010.5451386 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5451386</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5451386</ieee_id><sourcerecordid>5451386</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-6caee6e818ec590c81b57d2f5e3cf14d6c3fbc8254445d84d15b2f277b92d9c03</originalsourceid><addsrcrecordid>eNpNkEFLAzEQhSNSUGv_gF7yB1qTbJJNjnVpa6EgiJ5LNpml0d1syaaUevGvG2gF5zK8x7wP3iD0QMmMUqKf1lU1X8wYyVpwQQslr9BEl4pyxrkQSpbX_7XUZITuGCFaF0qV8gZNhuGT5Mlhpfgt-lmaQ5twgiHhvsEB0rGPX-DwcAp2F_vgv03yfcC2Dyn2bfaHBB2uTzjtILtd7cP5IsffnpcZcYim_SNhExzem5i8bQEPRxM73O-T7y7cezRqTDvA5LLH6GO5eK9eppvX1bqab6aeliJNpTUAEhRVYIUmVtFalI41AgrbUO6kLZraKiZ4ru0Ud1TUrGFlWWvmtCXFGD2euR4AtvvoOxNP28sPi19ptmZc</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Wang, Ting ; Wang, Heng ; Xie, Hao-fei</creator><creatorcontrib>Wang, Ting ; Wang, Heng ; Xie, Hao-fei</creatorcontrib><description>Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.</description><identifier>ISBN: 9781424455690</identifier><identifier>ISBN: 1424455693</identifier><identifier>ISBN: 1424455855</identifier><identifier>ISBN: 9781424455850</identifier><identifier>EISBN: 9781424455867</identifier><identifier>EISBN: 1424455863</identifier><identifier>DOI: 10.1109/ICCAE.2010.5451386</identifier><identifier>LCCN: 2009938876</identifier><language>eng</language><publisher>IEEE</publisher><subject>Control systems ; Fault diagnosis ; fault test ; Instruments ; Intelligent control ; Intelligent networks ; Laboratories ; networked synchronization control system ; neural network ; Neural networks ; Particle swarm optimization ; System testing ; Telecommunication control</subject><ispartof>2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, Vol.3, p.383-386</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/5451386$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5451386$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Wang, Heng</creatorcontrib><creatorcontrib>Xie, Hao-fei</creatorcontrib><title>Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization</title><title>2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)</title><addtitle>ICCAE</addtitle><description>Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.</description><subject>Control systems</subject><subject>Fault diagnosis</subject><subject>fault test</subject><subject>Instruments</subject><subject>Intelligent control</subject><subject>Intelligent networks</subject><subject>Laboratories</subject><subject>networked synchronization control system</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Particle swarm optimization</subject><subject>System testing</subject><subject>Telecommunication control</subject><isbn>9781424455690</isbn><isbn>1424455693</isbn><isbn>1424455855</isbn><isbn>9781424455850</isbn><isbn>9781424455867</isbn><isbn>1424455863</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNkEFLAzEQhSNSUGv_gF7yB1qTbJJNjnVpa6EgiJ5LNpml0d1syaaUevGvG2gF5zK8x7wP3iD0QMmMUqKf1lU1X8wYyVpwQQslr9BEl4pyxrkQSpbX_7XUZITuGCFaF0qV8gZNhuGT5Mlhpfgt-lmaQ5twgiHhvsEB0rGPX-DwcAp2F_vgv03yfcC2Dyn2bfaHBB2uTzjtILtd7cP5IsffnpcZcYim_SNhExzem5i8bQEPRxM73O-T7y7cezRqTDvA5LLH6GO5eK9eppvX1bqab6aeliJNpTUAEhRVYIUmVtFalI41AgrbUO6kLZraKiZ4ru0Ud1TUrGFlWWvmtCXFGD2euR4AtvvoOxNP28sPi19ptmZc</recordid><startdate>201002</startdate><enddate>201002</enddate><creator>Wang, Ting</creator><creator>Wang, Heng</creator><creator>Xie, Hao-fei</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201002</creationdate><title>Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization</title><author>Wang, Ting ; Wang, Heng ; Xie, Hao-fei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6caee6e818ec590c81b57d2f5e3cf14d6c3fbc8254445d84d15b2f277b92d9c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Control systems</topic><topic>Fault diagnosis</topic><topic>fault test</topic><topic>Instruments</topic><topic>Intelligent control</topic><topic>Intelligent networks</topic><topic>Laboratories</topic><topic>networked synchronization control system</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Particle swarm optimization</topic><topic>System testing</topic><topic>Telecommunication control</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Ting</creatorcontrib><creatorcontrib>Wang, Heng</creatorcontrib><creatorcontrib>Xie, Hao-fei</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>Wang, Ting</au><au>Wang, Heng</au><au>Xie, Hao-fei</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization</atitle><btitle>2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)</btitle><stitle>ICCAE</stitle><date>2010-02</date><risdate>2010</risdate><volume>3</volume><spage>383</spage><epage>386</epage><pages>383-386</pages><isbn>9781424455690</isbn><isbn>1424455693</isbn><isbn>1424455855</isbn><isbn>9781424455850</isbn><eisbn>9781424455867</eisbn><eisbn>1424455863</eisbn><abstract>Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.</abstract><pub>IEEE</pub><doi>10.1109/ICCAE.2010.5451386</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781424455690 |
| ispartof | 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, Vol.3, p.383-386 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5451386 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Control systems Fault diagnosis fault test Instruments Intelligent control Intelligent networks Laboratories networked synchronization control system neural network Neural networks Particle swarm optimization System testing Telecommunication control |
| title | Fault test of networked synchronization control system by the combination of RBF neural network and particle swarm optimization |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T20%3A43%3A31IST&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=Fault%20test%20of%20networked%20synchronization%20control%20system%20by%20the%20combination%20of%20RBF%20neural%20network%20and%20particle%20swarm%20optimization&rft.btitle=2010%20The%202nd%20International%20Conference%20on%20Computer%20and%20Automation%20Engineering%20(ICCAE)&rft.au=Wang,%20Ting&rft.date=2010-02&rft.volume=3&rft.spage=383&rft.epage=386&rft.pages=383-386&rft.isbn=9781424455690&rft.isbn_list=1424455693&rft.isbn_list=1424455855&rft.isbn_list=9781424455850&rft_id=info:doi/10.1109/ICCAE.2010.5451386&rft_dat=%3Cieee_6IE%3E5451386%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424455867&rft.eisbn_list=1424455863&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5451386&rfr_iscdi=true |