Hierarchical control structure for multisensor object recognition system
Multisensors can be used to overcome problems associated with intelligent object recognition systems. The introduction of multisensors into such a system emphasizes the need for an effective method for combining sensor outputs, and a complicated system requires intelligent control. We propose a hier...
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 | 814 vol.2 |
|---|---|
| container_issue | |
| container_start_page | 811 |
| container_title | |
| container_volume | 2 |
| creator | Hong, Chen Yongbao, He |
| description | Multisensors can be used to overcome problems associated with intelligent object recognition systems. The introduction of multisensors into such a system emphasizes the need for an effective method for combining sensor outputs, and a complicated system requires intelligent control. We propose a hierarchical control structure (HCS) to build a recognition system and introduce a fuzzy neural net (FNN) method to fuse multisensor information. This paper describes the concept of HCS, the derivation of fuzzy rules, the FNN's construction and training, etc. Then, experimental results involving parts recognition in the application of an assembly robot are presented.< > |
| doi_str_mv | 10.1109/TENCON.1993.320137 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>proquest_6IE</sourceid><recordid>TN_cdi_ieee_primary_320137</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>320137</ieee_id><sourcerecordid>26067424</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-859dd8d6b03ad60ab2d0ae46746e8c3f8df7943956db3977c804252868c0f65c3</originalsourceid><addsrcrecordid>eNotkLFOwzAURS0hJKD0BzplYkux_RzHHlFVKFLVLmWOHPsFXCVxsZ2hf0-kcpd7h6MzXEJWjK4Zo_r1tD1sjoc10xrWwCmD-o480VpRYBwAHsgypTOdIwRIwR7Jbucxmmh_vDV9YcOYY-iLlONk8xSx6EIshqnPPuGY5h3aM9pcRLThe_TZh7FI15RxeCb3nekTLv97Qb7et6fNrtwfPz43b_vScwq5VJV2TjnZUjBOUtNyRw0KWQuJykKnXFdrAbqSrgVd11ZRwSuupLK0k5WFBXm5eS8x_E6YcjP4ZLHvzYhhSg2XdJZxMYOrG-gRsblEP5h4bW6nwB_sC1kV</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>26067424</pqid></control><display><type>conference_proceeding</type><title>Hierarchical control structure for multisensor object recognition system</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Hong, Chen ; Yongbao, He</creator><creatorcontrib>Hong, Chen ; Yongbao, He</creatorcontrib><description>Multisensors can be used to overcome problems associated with intelligent object recognition systems. The introduction of multisensors into such a system emphasizes the need for an effective method for combining sensor outputs, and a complicated system requires intelligent control. We propose a hierarchical control structure (HCS) to build a recognition system and introduce a fuzzy neural net (FNN) method to fuse multisensor information. This paper describes the concept of HCS, the derivation of fuzzy rules, the FNN's construction and training, etc. Then, experimental results involving parts recognition in the application of an assembly robot are presented.< ></description><identifier>ISBN: 0780312333</identifier><identifier>ISBN: 9780780312333</identifier><identifier>DOI: 10.1109/TENCON.1993.320137</identifier><language>eng</language><publisher>IEEE</publisher><subject>Control systems ; Fuzzy control ; Fuzzy neural networks ; Fuzzy systems ; Intelligent control ; Intelligent sensors ; Intelligent systems ; Neural networks ; Object recognition ; Sensor systems</subject><ispartof>Proceedings / TENCON '93, 1993 IEEE Region 10 Conference on "Computer, Communication, Control, and Power Engineering", October 19-21, 1993, Beijing, Beijing International Convention Center, Beijing Continental Grand Hotel, 1993, Vol.2, p.811-814 vol.2</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/320137$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,780,784,789,790,2058,4050,4051,27924,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/320137$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Hong, Chen</creatorcontrib><creatorcontrib>Yongbao, He</creatorcontrib><title>Hierarchical control structure for multisensor object recognition system</title><title>Proceedings / TENCON '93, 1993 IEEE Region 10 Conference on "Computer, Communication, Control, and Power Engineering", October 19-21, 1993, Beijing, Beijing International Convention Center, Beijing Continental Grand Hotel</title><addtitle>TENCON</addtitle><description>Multisensors can be used to overcome problems associated with intelligent object recognition systems. The introduction of multisensors into such a system emphasizes the need for an effective method for combining sensor outputs, and a complicated system requires intelligent control. We propose a hierarchical control structure (HCS) to build a recognition system and introduce a fuzzy neural net (FNN) method to fuse multisensor information. This paper describes the concept of HCS, the derivation of fuzzy rules, the FNN's construction and training, etc. Then, experimental results involving parts recognition in the application of an assembly robot are presented.< ></description><subject>Control systems</subject><subject>Fuzzy control</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy systems</subject><subject>Intelligent control</subject><subject>Intelligent sensors</subject><subject>Intelligent systems</subject><subject>Neural networks</subject><subject>Object recognition</subject><subject>Sensor systems</subject><isbn>0780312333</isbn><isbn>9780780312333</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1993</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkLFOwzAURS0hJKD0BzplYkux_RzHHlFVKFLVLmWOHPsFXCVxsZ2hf0-kcpd7h6MzXEJWjK4Zo_r1tD1sjoc10xrWwCmD-o480VpRYBwAHsgypTOdIwRIwR7Jbucxmmh_vDV9YcOYY-iLlONk8xSx6EIshqnPPuGY5h3aM9pcRLThe_TZh7FI15RxeCb3nekTLv97Qb7et6fNrtwfPz43b_vScwq5VJV2TjnZUjBOUtNyRw0KWQuJykKnXFdrAbqSrgVd11ZRwSuupLK0k5WFBXm5eS8x_E6YcjP4ZLHvzYhhSg2XdJZxMYOrG-gRsblEP5h4bW6nwB_sC1kV</recordid><startdate>1993</startdate><enddate>1993</enddate><creator>Hong, Chen</creator><creator>Yongbao, He</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>1993</creationdate><title>Hierarchical control structure for multisensor object recognition system</title><author>Hong, Chen ; Yongbao, He</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-859dd8d6b03ad60ab2d0ae46746e8c3f8df7943956db3977c804252868c0f65c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Control systems</topic><topic>Fuzzy control</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy systems</topic><topic>Intelligent control</topic><topic>Intelligent sensors</topic><topic>Intelligent systems</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Sensor systems</topic><toplevel>online_resources</toplevel><creatorcontrib>Hong, Chen</creatorcontrib><creatorcontrib>Yongbao, He</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><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hong, Chen</au><au>Yongbao, He</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hierarchical control structure for multisensor object recognition system</atitle><btitle>Proceedings / TENCON '93, 1993 IEEE Region 10 Conference on "Computer, Communication, Control, and Power Engineering", October 19-21, 1993, Beijing, Beijing International Convention Center, Beijing Continental Grand Hotel</btitle><stitle>TENCON</stitle><date>1993</date><risdate>1993</risdate><volume>2</volume><spage>811</spage><epage>814 vol.2</epage><pages>811-814 vol.2</pages><isbn>0780312333</isbn><isbn>9780780312333</isbn><abstract>Multisensors can be used to overcome problems associated with intelligent object recognition systems. The introduction of multisensors into such a system emphasizes the need for an effective method for combining sensor outputs, and a complicated system requires intelligent control. We propose a hierarchical control structure (HCS) to build a recognition system and introduce a fuzzy neural net (FNN) method to fuse multisensor information. This paper describes the concept of HCS, the derivation of fuzzy rules, the FNN's construction and training, etc. Then, experimental results involving parts recognition in the application of an assembly robot are presented.< ></abstract><pub>IEEE</pub><doi>10.1109/TENCON.1993.320137</doi></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 0780312333 |
| ispartof | Proceedings / TENCON '93, 1993 IEEE Region 10 Conference on "Computer, Communication, Control, and Power Engineering", October 19-21, 1993, Beijing, Beijing International Convention Center, Beijing Continental Grand Hotel, 1993, Vol.2, p.811-814 vol.2 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_320137 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Control systems Fuzzy control Fuzzy neural networks Fuzzy systems Intelligent control Intelligent sensors Intelligent systems Neural networks Object recognition Sensor systems |
| title | Hierarchical control structure for multisensor object recognition system |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T09%3A31%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Hierarchical%20control%20structure%20for%20multisensor%20object%20recognition%20system&rft.btitle=Proceedings%20/%20TENCON%20'93,%201993%20IEEE%20Region%2010%20Conference%20on%20%22Computer,%20Communication,%20Control,%20and%20Power%20Engineering%22,%20October%2019-21,%201993,%20Beijing,%20Beijing%20International%20Convention%20Center,%20Beijing%20Continental%20Grand%20Hotel&rft.au=Hong,%20Chen&rft.date=1993&rft.volume=2&rft.spage=811&rft.epage=814%20vol.2&rft.pages=811-814%20vol.2&rft.isbn=0780312333&rft.isbn_list=9780780312333&rft_id=info:doi/10.1109/TENCON.1993.320137&rft_dat=%3Cproquest_6IE%3E26067424%3C/proquest_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=26067424&rft_id=info:pmid/&rft_ieee_id=320137&rfr_iscdi=true |