Knowledge modeling for classical control theory based on neural network
The design is mainly directed against neural network has a strong nonlinear mapping ability to be effective in the expression of expertise and know-how to the establishment of empirical knowledge of experts from the input space to the output of the nonlinear mapping space. Classic control theory, su...
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 | 161 |
|---|---|
| container_issue | |
| container_start_page | 158 |
| container_title | |
| container_volume | |
| creator | Jun Yi Taifu Li Jike Ge Yingying Su Wenjin Hu |
| description | The design is mainly directed against neural network has a strong nonlinear mapping ability to be effective in the expression of expertise and know-how to the establishment of empirical knowledge of experts from the input space to the output of the nonlinear mapping space. Classic control theory, such as root locus method and frequency response methods, are also called by experience and knowledge of experts. Therefore, this issue is envisaged that the use of the function of neural networks to solve classical correction control system to solve the problem of controller parameters. |
| doi_str_mv | 10.1109/WCICA.2011.5970719 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5970719</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5970719</ieee_id><sourcerecordid>5970719</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-ac67d2e806586aebfb913f7370e2afeba04bc51d72cc02eff2c81c725a0152da3</originalsourceid><addsrcrecordid>eNpFj89KAzEYxCMiqLUvoJe8wNZ82c2_oyzaigUvit5KNvlSV7eJJCulb--CBecyDD8YZgi5BrYAYOb2rX1s7xacASyEUUyBOSGXIIHrRhqjT_-Dfj8n81I-2SQpjWr0BVk-xbQf0G-R7pLHoY9bGlKmbrCl9M4O1KU45jTQ8QNTPtDOFvQ0RRrxJ0844rhP-euKnAU7FJwffUZeH-5f2lW1fl5O-9ZVD0qMlXVSeY6aSaGlxS50BuqgasWQ24CdZU3nBHjFnWMcQ-BOg1NcWAaCe1vPyM1fb4-Im-_c72w-bI7H61-C804a</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Knowledge modeling for classical control theory based on neural network</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Jun Yi ; Taifu Li ; Jike Ge ; Yingying Su ; Wenjin Hu</creator><creatorcontrib>Jun Yi ; Taifu Li ; Jike Ge ; Yingying Su ; Wenjin Hu</creatorcontrib><description>The design is mainly directed against neural network has a strong nonlinear mapping ability to be effective in the expression of expertise and know-how to the establishment of empirical knowledge of experts from the input space to the output of the nonlinear mapping space. Classic control theory, such as root locus method and frequency response methods, are also called by experience and knowledge of experts. Therefore, this issue is envisaged that the use of the function of neural networks to solve classical correction control system to solve the problem of controller parameters.</description><identifier>ISBN: 161284698X</identifier><identifier>ISBN: 9781612846989</identifier><identifier>EISBN: 1612846998</identifier><identifier>EISBN: 1612847005</identifier><identifier>EISBN: 9781612847009</identifier><identifier>EISBN: 9781612846996</identifier><identifier>DOI: 10.1109/WCICA.2011.5970719</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Classical control theory ; Computational modeling ; Control systems ; Control theory ; controller parameters ; Knowledge engineering ; Knowledge model ; Mathematical model ; Neural networks ; Training</subject><ispartof>2011 9th World Congress on Intelligent Control and Automation, 2011, p.158-161</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/5970719$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5970719$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jun Yi</creatorcontrib><creatorcontrib>Taifu Li</creatorcontrib><creatorcontrib>Jike Ge</creatorcontrib><creatorcontrib>Yingying Su</creatorcontrib><creatorcontrib>Wenjin Hu</creatorcontrib><title>Knowledge modeling for classical control theory based on neural network</title><title>2011 9th World Congress on Intelligent Control and Automation</title><addtitle>WCICA</addtitle><description>The design is mainly directed against neural network has a strong nonlinear mapping ability to be effective in the expression of expertise and know-how to the establishment of empirical knowledge of experts from the input space to the output of the nonlinear mapping space. Classic control theory, such as root locus method and frequency response methods, are also called by experience and knowledge of experts. Therefore, this issue is envisaged that the use of the function of neural networks to solve classical correction control system to solve the problem of controller parameters.</description><subject>Artificial neural networks</subject><subject>Classical control theory</subject><subject>Computational modeling</subject><subject>Control systems</subject><subject>Control theory</subject><subject>controller parameters</subject><subject>Knowledge engineering</subject><subject>Knowledge model</subject><subject>Mathematical model</subject><subject>Neural networks</subject><subject>Training</subject><isbn>161284698X</isbn><isbn>9781612846989</isbn><isbn>1612846998</isbn><isbn>1612847005</isbn><isbn>9781612847009</isbn><isbn>9781612846996</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFj89KAzEYxCMiqLUvoJe8wNZ82c2_oyzaigUvit5KNvlSV7eJJCulb--CBecyDD8YZgi5BrYAYOb2rX1s7xacASyEUUyBOSGXIIHrRhqjT_-Dfj8n81I-2SQpjWr0BVk-xbQf0G-R7pLHoY9bGlKmbrCl9M4O1KU45jTQ8QNTPtDOFvQ0RRrxJ0844rhP-euKnAU7FJwffUZeH-5f2lW1fl5O-9ZVD0qMlXVSeY6aSaGlxS50BuqgasWQ24CdZU3nBHjFnWMcQ-BOg1NcWAaCe1vPyM1fb4-Im-_c72w-bI7H61-C804a</recordid><startdate>201106</startdate><enddate>201106</enddate><creator>Jun Yi</creator><creator>Taifu Li</creator><creator>Jike Ge</creator><creator>Yingying Su</creator><creator>Wenjin Hu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201106</creationdate><title>Knowledge modeling for classical control theory based on neural network</title><author>Jun Yi ; Taifu Li ; Jike Ge ; Yingying Su ; Wenjin Hu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-ac67d2e806586aebfb913f7370e2afeba04bc51d72cc02eff2c81c725a0152da3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Classical control theory</topic><topic>Computational modeling</topic><topic>Control systems</topic><topic>Control theory</topic><topic>controller parameters</topic><topic>Knowledge engineering</topic><topic>Knowledge model</topic><topic>Mathematical model</topic><topic>Neural networks</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Jun Yi</creatorcontrib><creatorcontrib>Taifu Li</creatorcontrib><creatorcontrib>Jike Ge</creatorcontrib><creatorcontrib>Yingying Su</creatorcontrib><creatorcontrib>Wenjin Hu</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>Jun Yi</au><au>Taifu Li</au><au>Jike Ge</au><au>Yingying Su</au><au>Wenjin Hu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Knowledge modeling for classical control theory based on neural network</atitle><btitle>2011 9th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2011-06</date><risdate>2011</risdate><spage>158</spage><epage>161</epage><pages>158-161</pages><isbn>161284698X</isbn><isbn>9781612846989</isbn><eisbn>1612846998</eisbn><eisbn>1612847005</eisbn><eisbn>9781612847009</eisbn><eisbn>9781612846996</eisbn><abstract>The design is mainly directed against neural network has a strong nonlinear mapping ability to be effective in the expression of expertise and know-how to the establishment of empirical knowledge of experts from the input space to the output of the nonlinear mapping space. Classic control theory, such as root locus method and frequency response methods, are also called by experience and knowledge of experts. Therefore, this issue is envisaged that the use of the function of neural networks to solve classical correction control system to solve the problem of controller parameters.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2011.5970719</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 161284698X |
| ispartof | 2011 9th World Congress on Intelligent Control and Automation, 2011, p.158-161 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_5970719 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Classical control theory Computational modeling Control systems Control theory controller parameters Knowledge engineering Knowledge model Mathematical model Neural networks Training |
| title | Knowledge modeling for classical control theory based on neural network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T16%3A02%3A05IST&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=Knowledge%20modeling%20for%20classical%20control%20theory%20based%20on%20neural%20network&rft.btitle=2011%209th%20World%20Congress%20on%20Intelligent%20Control%20and%20Automation&rft.au=Jun%20Yi&rft.date=2011-06&rft.spage=158&rft.epage=161&rft.pages=158-161&rft.isbn=161284698X&rft.isbn_list=9781612846989&rft_id=info:doi/10.1109/WCICA.2011.5970719&rft_dat=%3Cieee_6IE%3E5970719%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1612846998&rft.eisbn_list=1612847005&rft.eisbn_list=9781612847009&rft.eisbn_list=9781612846996&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5970719&rfr_iscdi=true |