Neural controllers for systems with unknown dynamics
This study presents a methodology for specifying a neural controller for a system about which no a priori model information is available. The neural design presumes that a finite duration input/output (I/O) histogram on the system is available. The design procedure extracts from the histogram suffic...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 1995-10, Vol.31 (4), p.1331-1340 |
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| container_title | IEEE transactions on aerospace and electronic systems |
| container_volume | 31 |
| creator | Porter, W.A. Wie Liu |
| description | This study presents a methodology for specifying a neural controller for a system about which no a priori model information is available. The neural design presumes that a finite duration input/output (I/O) histogram on the system is available. The design procedure extracts from the histogram sufficient information to specify the neural feedback controller. The resultant controller will drive the system along a general output reference profile (unknown during the design). The resultant controller also exhibits the capability of disturbance rejection and the capacity to stabilize unstable plants.< > |
| doi_str_mv | 10.1109/7.464354 |
| format | Article |
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Neural networks ; Control system synthesis ; Control systems ; Exact sciences and technology ; Histograms ; Optical computing ; State-space methods ; Trajectory ; Very large scale integration</subject><ispartof>IEEE transactions on aerospace and electronic systems, 1995-10, Vol.31 (4), p.1331-1340</ispartof><rights>1995 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c366t-9d5d359257165980dbfdfe123492cc6bdcf9bb9916f828d3018fbb33642a364b3</citedby><cites>FETCH-LOGICAL-c366t-9d5d359257165980dbfdfe123492cc6bdcf9bb9916f828d3018fbb33642a364b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/464354$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/464354$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=3702318$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Porter, W.A.</creatorcontrib><creatorcontrib>Wie Liu</creatorcontrib><title>Neural controllers for systems with unknown dynamics</title><title>IEEE transactions on aerospace and electronic systems</title><addtitle>T-AES</addtitle><description>This study presents a methodology for specifying a neural controller for a system about which no a priori model information is available. The neural design presumes that a finite duration input/output (I/O) histogram on the system is available. The design procedure extracts from the histogram sufficient information to specify the neural feedback controller. The resultant controller will drive the system along a general output reference profile (unknown during the design). The resultant controller also exhibits the capability of disturbance rejection and the capacity to stabilize unstable plants.< ></description><subject>Adaptive control</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Computational modeling</subject><subject>Computer science; control theory; systems</subject><subject>Connectionism. Neural networks</subject><subject>Control system synthesis</subject><subject>Control systems</subject><subject>Exact sciences and technology</subject><subject>Histograms</subject><subject>Optical computing</subject><subject>State-space methods</subject><subject>Trajectory</subject><subject>Very large scale integration</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1995</creationdate><recordtype>article</recordtype><recordid>eNqNkMtLAzEQh4MoWKvg2dMeRLxszWvzOErxBUUveg5JNsHVfdTMLqX_vatbeu5lhmE-vhl-CF0SvCAE6zu54IKzgh-hGSkKmWuB2TGaYUxUrmlBTtEZwNc4csXZDPHXMCRbZ75r-9TVdUiQxS5lsIU-NJBtqv4zG9rvttu0WbltbVN5OEcn0dYQLnZ9jj4eH96Xz_nq7elleb_KPROiz3VZlKwYj0oiCq1w6WIZA6GMa-q9cKWP2jmtiYiKqpKNL0bnGBOc2rE4Nkc3k3edup8hQG-aCnyoa9uGbgBDlZZKKnoAyCUl-ABQcqH5P3g7gT51AClEs05VY9PWEGz-gjbSTEGP6PXOacHbOibb-gr2PJOjjqgRu5qwKoSw3-4cv3KBg8E</recordid><startdate>19951001</startdate><enddate>19951001</enddate><creator>Porter, W.A.</creator><creator>Wie Liu</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7SP</scope><scope>7TB</scope><scope>FR3</scope></search><sort><creationdate>19951001</creationdate><title>Neural controllers for systems with unknown dynamics</title><author>Porter, W.A. ; Wie Liu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c366t-9d5d359257165980dbfdfe123492cc6bdcf9bb9916f828d3018fbb33642a364b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1995</creationdate><topic>Adaptive control</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Computational modeling</topic><topic>Computer science; control theory; systems</topic><topic>Connectionism. Neural networks</topic><topic>Control system synthesis</topic><topic>Control systems</topic><topic>Exact sciences and technology</topic><topic>Histograms</topic><topic>Optical computing</topic><topic>State-space methods</topic><topic>Trajectory</topic><topic>Very large scale integration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Porter, W.A.</creatorcontrib><creatorcontrib>Wie Liu</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on aerospace and electronic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Porter, W.A.</au><au>Wie Liu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural controllers for systems with unknown dynamics</atitle><jtitle>IEEE transactions on aerospace and electronic systems</jtitle><stitle>T-AES</stitle><date>1995-10-01</date><risdate>1995</risdate><volume>31</volume><issue>4</issue><spage>1331</spage><epage>1340</epage><pages>1331-1340</pages><issn>0018-9251</issn><eissn>1557-9603</eissn><coden>IEARAX</coden><abstract>This study presents a methodology for specifying a neural controller for a system about which no a priori model information is available. The neural design presumes that a finite duration input/output (I/O) histogram on the system is available. The design procedure extracts from the histogram sufficient information to specify the neural feedback controller. The resultant controller will drive the system along a general output reference profile (unknown during the design). The resultant controller also exhibits the capability of disturbance rejection and the capacity to stabilize unstable plants.< ></abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/7.464354</doi><tpages>10</tpages></addata></record> |
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| ispartof | IEEE transactions on aerospace and electronic systems, 1995-10, Vol.31 (4), p.1331-1340 |
| issn | 0018-9251 1557-9603 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_28472102 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive control Applied sciences Artificial intelligence Artificial neural networks Computational modeling Computer science control theory systems Connectionism. Neural networks Control system synthesis Control systems Exact sciences and technology Histograms Optical computing State-space methods Trajectory Very large scale integration |
| title | Neural controllers for systems with unknown dynamics |
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