Distributed logic processors trained under constraints using stochastic approximation techniques
The paper concerns the estimation under constraints of the parameters of distributed logic processors (DLP). This optimization problem under constraints is solved using stochastic approximation techniques. DLPs are fuzzy neural networks capable of representing nonlinear functions. They consist of se...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 1999-07, Vol.29 (4), p.421-426 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 426 |
|---|---|
| container_issue | 4 |
| container_start_page | 421 |
| container_title | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans |
| container_volume | 29 |
| creator | Najim, K. Ikonen, E. |
| description | The paper concerns the estimation under constraints of the parameters of distributed logic processors (DLP). This optimization problem under constraints is solved using stochastic approximation techniques. DLPs are fuzzy neural networks capable of representing nonlinear functions. They consist of several logic processors, each of which performs a logical fuzzy mapping. A simulation example, using data collected from an industrial fluidized bed combustor, illustrates the feasibility and the performance of this training algorithm. |
| doi_str_mv | 10.1109/3468.769763 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_miscellaneous_919928902</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>769763</ieee_id><sourcerecordid>28222939</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-6325641b6c8a0d83d1c5b41fd8dd2df1faff62d27e7e7335a30e30000d5f96ff3</originalsourceid><addsrcrecordid>eNqN0TtPwzAQAOAIgUQpTGxMmWBAKX7FiUdUnlIlFpiD60drlMbF50jw73FIxQjYgy3fd7Z1l2WnGM0wRuKKMl7PKi4qTveyCS7LuiCM8P20RzUtGCPVYXYE8IYQZkywSfZ64yAGt-yj0XnrV07l2-CVAfAB8hik61Kg77QJufIdfJ9EyHtw3SqH6NVaQkxZcpvyPtxGRue7PBq17tx7b-A4O7CyBXOyW6fZy93t8_yhWDzdP86vF4WiVRkLTknJGV5yVUuka6qxKpcMW11rTbTFVlrLiSaVSZPSUlJkKEpDl1Zwa-k0uxjvTd8Y3o3NxoEybSs743toBBaC1AKRJM9_lYOqUnH-AQkhgoq_IReEYFImeDlCFTxAMLbZhlSy8Nlg1AwdbIYONmMHkz4btTPG_Mhd8AsJwJha</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>26922125</pqid></control><display><type>article</type><title>Distributed logic processors trained under constraints using stochastic approximation techniques</title><source>IEEE Electronic Library (IEL)</source><creator>Najim, K. ; Ikonen, E.</creator><creatorcontrib>Najim, K. ; Ikonen, E.</creatorcontrib><description>The paper concerns the estimation under constraints of the parameters of distributed logic processors (DLP). This optimization problem under constraints is solved using stochastic approximation techniques. DLPs are fuzzy neural networks capable of representing nonlinear functions. They consist of several logic processors, each of which performs a logical fuzzy mapping. A simulation example, using data collected from an industrial fluidized bed combustor, illustrates the feasibility and the performance of this training algorithm.</description><identifier>ISSN: 1083-4427</identifier><identifier>EISSN: 1558-2426</identifier><identifier>DOI: 10.1109/3468.769763</identifier><identifier>CODEN: ITSHFX</identifier><language>eng</language><publisher>IEEE</publisher><subject>Computer simulation ; Constraint optimization ; Fuzzy ; Fuzzy logic ; Fuzzy neural networks ; Fuzzy set theory ; Humans ; Industrial training ; Laboratories ; Logic ; Mathematical analysis ; Parameter estimation ; Process control ; Processors ; Shape control ; Stochastic processes ; Stochasticity</subject><ispartof>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 1999-07, Vol.29 (4), p.421-426</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-6325641b6c8a0d83d1c5b41fd8dd2df1faff62d27e7e7335a30e30000d5f96ff3</citedby><cites>FETCH-LOGICAL-c375t-6325641b6c8a0d83d1c5b41fd8dd2df1faff62d27e7e7335a30e30000d5f96ff3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/769763$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/769763$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Najim, K.</creatorcontrib><creatorcontrib>Ikonen, E.</creatorcontrib><title>Distributed logic processors trained under constraints using stochastic approximation techniques</title><title>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</title><addtitle>TSMCA</addtitle><description>The paper concerns the estimation under constraints of the parameters of distributed logic processors (DLP). This optimization problem under constraints is solved using stochastic approximation techniques. DLPs are fuzzy neural networks capable of representing nonlinear functions. They consist of several logic processors, each of which performs a logical fuzzy mapping. A simulation example, using data collected from an industrial fluidized bed combustor, illustrates the feasibility and the performance of this training algorithm.</description><subject>Computer simulation</subject><subject>Constraint optimization</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy set theory</subject><subject>Humans</subject><subject>Industrial training</subject><subject>Laboratories</subject><subject>Logic</subject><subject>Mathematical analysis</subject><subject>Parameter estimation</subject><subject>Process control</subject><subject>Processors</subject><subject>Shape control</subject><subject>Stochastic processes</subject><subject>Stochasticity</subject><issn>1083-4427</issn><issn>1558-2426</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqN0TtPwzAQAOAIgUQpTGxMmWBAKX7FiUdUnlIlFpiD60drlMbF50jw73FIxQjYgy3fd7Z1l2WnGM0wRuKKMl7PKi4qTveyCS7LuiCM8P20RzUtGCPVYXYE8IYQZkywSfZ64yAGt-yj0XnrV07l2-CVAfAB8hik61Kg77QJufIdfJ9EyHtw3SqH6NVaQkxZcpvyPtxGRue7PBq17tx7b-A4O7CyBXOyW6fZy93t8_yhWDzdP86vF4WiVRkLTknJGV5yVUuka6qxKpcMW11rTbTFVlrLiSaVSZPSUlJkKEpDl1Zwa-k0uxjvTd8Y3o3NxoEybSs743toBBaC1AKRJM9_lYOqUnH-AQkhgoq_IReEYFImeDlCFTxAMLbZhlSy8Nlg1AwdbIYONmMHkz4btTPG_Mhd8AsJwJha</recordid><startdate>19990701</startdate><enddate>19990701</enddate><creator>Najim, K.</creator><creator>Ikonen, E.</creator><general>IEEE</general><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7SP</scope><scope>7TB</scope><scope>FR3</scope><scope>H8D</scope><scope>F28</scope></search><sort><creationdate>19990701</creationdate><title>Distributed logic processors trained under constraints using stochastic approximation techniques</title><author>Najim, K. ; Ikonen, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-6325641b6c8a0d83d1c5b41fd8dd2df1faff62d27e7e7335a30e30000d5f96ff3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Computer simulation</topic><topic>Constraint optimization</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy set theory</topic><topic>Humans</topic><topic>Industrial training</topic><topic>Laboratories</topic><topic>Logic</topic><topic>Mathematical analysis</topic><topic>Parameter estimation</topic><topic>Process control</topic><topic>Processors</topic><topic>Shape control</topic><topic>Stochastic processes</topic><topic>Stochasticity</topic><toplevel>online_resources</toplevel><creatorcontrib>Najim, K.</creatorcontrib><creatorcontrib>Ikonen, E.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</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><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Najim, K.</au><au>Ikonen, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributed logic processors trained under constraints using stochastic approximation techniques</atitle><jtitle>IEEE transactions on systems, man and cybernetics. Part A, Systems and humans</jtitle><stitle>TSMCA</stitle><date>1999-07-01</date><risdate>1999</risdate><volume>29</volume><issue>4</issue><spage>421</spage><epage>426</epage><pages>421-426</pages><issn>1083-4427</issn><eissn>1558-2426</eissn><coden>ITSHFX</coden><abstract>The paper concerns the estimation under constraints of the parameters of distributed logic processors (DLP). This optimization problem under constraints is solved using stochastic approximation techniques. DLPs are fuzzy neural networks capable of representing nonlinear functions. They consist of several logic processors, each of which performs a logical fuzzy mapping. A simulation example, using data collected from an industrial fluidized bed combustor, illustrates the feasibility and the performance of this training algorithm.</abstract><pub>IEEE</pub><doi>10.1109/3468.769763</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1083-4427 |
| ispartof | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans, 1999-07, Vol.29 (4), p.421-426 |
| issn | 1083-4427 1558-2426 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_919928902 |
| source | IEEE Electronic Library (IEL) |
| subjects | Computer simulation Constraint optimization Fuzzy Fuzzy logic Fuzzy neural networks Fuzzy set theory Humans Industrial training Laboratories Logic Mathematical analysis Parameter estimation Process control Processors Shape control Stochastic processes Stochasticity |
| title | Distributed logic processors trained under constraints using stochastic approximation techniques |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T13%3A17%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Distributed%20logic%20processors%20trained%20under%20constraints%20using%20stochastic%20approximation%20techniques&rft.jtitle=IEEE%20transactions%20on%20systems,%20man%20and%20cybernetics.%20Part%20A,%20Systems%20and%20humans&rft.au=Najim,%20K.&rft.date=1999-07-01&rft.volume=29&rft.issue=4&rft.spage=421&rft.epage=426&rft.pages=421-426&rft.issn=1083-4427&rft.eissn=1558-2426&rft.coden=ITSHFX&rft_id=info:doi/10.1109/3468.769763&rft_dat=%3Cproquest_RIE%3E28222939%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=26922125&rft_id=info:pmid/&rft_ieee_id=769763&rfr_iscdi=true |