Combined hybrid clustering techniques and neural fuzzy networks to predict diesel engine emissions
This paper presents a neural fuzzy modeling approach based on hybrid clustering technique to predict a diesel engine's NOx emissions. A hybrid clustering algorithm is provided. Since the combustion process is very complicated, therefore, it is almost impossible to find a simple and accurate fir...
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| creator | Deng, Jiamei Stobart, Richard Plianos, Alexnndros |
| description | This paper presents a neural fuzzy modeling approach based on hybrid clustering technique to predict a diesel engine's NOx emissions. A hybrid clustering algorithm is provided. Since the combustion process is very complicated, therefore, it is almost impossible to find a simple and accurate first principle model to predict diesel emissions. Black-box models implementing Artificial Intelligent Techniques must be developed. Fuzzy modeling seems to be one of the most suitable approach for modeling diesel emissions with big oscillations and high frequency. Clustering is used with fuzzy modeling approach for determining fuzzy if-then rules, so that a fuzzy network, trained with back propagation, adjusts the centers and widths of the membership function. This paper uses hybrid clustering techniques to build a neural fuzzy model successfully. The results show that the model has very good accuracy in predicting diesel engine's NOx emissions. |
| doi_str_mv | 10.1109/ICSMC.2007.4413857 |
| format | Conference Proceeding |
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A hybrid clustering algorithm is provided. Since the combustion process is very complicated, therefore, it is almost impossible to find a simple and accurate first principle model to predict diesel emissions. Black-box models implementing Artificial Intelligent Techniques must be developed. Fuzzy modeling seems to be one of the most suitable approach for modeling diesel emissions with big oscillations and high frequency. Clustering is used with fuzzy modeling approach for determining fuzzy if-then rules, so that a fuzzy network, trained with back propagation, adjusts the centers and widths of the membership function. This paper uses hybrid clustering techniques to build a neural fuzzy model successfully. The results show that the model has very good accuracy in predicting diesel engine's NOx emissions.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 142440990X</identifier><identifier>ISBN: 9781424409907</identifier><identifier>EISSN: 2577-1655</identifier><identifier>EISBN: 9781424409914</identifier><identifier>EISBN: 1424409918</identifier><identifier>DOI: 10.1109/ICSMC.2007.4413857</identifier><identifier>LCCN: 2007920351</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial intelligence ; Artificial neural networks ; Clustering algorithms ; clustering techniques ; Combustion ; Computational fluid dynamics ; Design engineering ; diesel engine ; Diesel engines ; emissions ; Frequency ; Fuzzy neural networks ; membership function ; neural fuzzy network ; Predictive models</subject><ispartof>2007 IEEE International Conference on Systems, Man and Cybernetics, 2007, p.3609-3614</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/4413857$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54899</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4413857$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Deng, Jiamei</creatorcontrib><creatorcontrib>Stobart, Richard</creatorcontrib><creatorcontrib>Plianos, Alexnndros</creatorcontrib><title>Combined hybrid clustering techniques and neural fuzzy networks to predict diesel engine emissions</title><title>2007 IEEE International Conference on Systems, Man and Cybernetics</title><addtitle>ICSMC</addtitle><description>This paper presents a neural fuzzy modeling approach based on hybrid clustering technique to predict a diesel engine's NOx emissions. 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The results show that the model has very good accuracy in predicting diesel engine's NOx emissions.</description><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Clustering algorithms</subject><subject>clustering techniques</subject><subject>Combustion</subject><subject>Computational fluid dynamics</subject><subject>Design engineering</subject><subject>diesel engine</subject><subject>Diesel engines</subject><subject>emissions</subject><subject>Frequency</subject><subject>Fuzzy neural networks</subject><subject>membership function</subject><subject>neural fuzzy network</subject><subject>Predictive models</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>142440990X</isbn><isbn>9781424409907</isbn><isbn>9781424409914</isbn><isbn>1424409918</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo10M1OwkAUBeDxh0RAXkA38wLFe-en7SxNI0qCcSELdmTauYXR0mKnjYGnFyOuTk5O8i0OY3cIU0QwD_Ps_TWbCoBkqhTKVCcXbGKSFJVQCoxBdcmGQidJhLHWV2z0P8Dqmg0RYhEZIVYDNvo1jACp8YaNQvgAEKAwHbI8a3a5r8nx7SFvveNF1YeOWl9veEfFtvZfPQVua8dr6ltb8bI_Hg-n0n037WfgXcP3LTlfdNx5ClRxqjcnkNPOh-CbOtyyQWmrQJNzjtly9rTMXqLF2_M8e1xE3kAXEaVWG8hjhU4WBkqHhFBIpYxVkDidOpWmaJQoQSaxVRYwprywFqQjU8oxu_9jPRGt963f2fawPv8mfwAKyF2G</recordid><startdate>200710</startdate><enddate>200710</enddate><creator>Deng, Jiamei</creator><creator>Stobart, Richard</creator><creator>Plianos, Alexnndros</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200710</creationdate><title>Combined hybrid clustering techniques and neural fuzzy networks to predict diesel engine emissions</title><author>Deng, Jiamei ; Stobart, Richard ; Plianos, Alexnndros</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-ee8a590b641d3c90fd1e10c3449a407d58d4881942f0376a4a016ebcaa03de9f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Clustering algorithms</topic><topic>clustering techniques</topic><topic>Combustion</topic><topic>Computational fluid dynamics</topic><topic>Design engineering</topic><topic>diesel engine</topic><topic>Diesel engines</topic><topic>emissions</topic><topic>Frequency</topic><topic>Fuzzy neural networks</topic><topic>membership function</topic><topic>neural fuzzy network</topic><topic>Predictive models</topic><toplevel>online_resources</toplevel><creatorcontrib>Deng, Jiamei</creatorcontrib><creatorcontrib>Stobart, Richard</creatorcontrib><creatorcontrib>Plianos, Alexnndros</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Deng, Jiamei</au><au>Stobart, Richard</au><au>Plianos, Alexnndros</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Combined hybrid clustering techniques and neural fuzzy networks to predict diesel engine emissions</atitle><btitle>2007 IEEE International Conference on Systems, Man and Cybernetics</btitle><stitle>ICSMC</stitle><date>2007-10</date><risdate>2007</risdate><spage>3609</spage><epage>3614</epage><pages>3609-3614</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>142440990X</isbn><isbn>9781424409907</isbn><eisbn>9781424409914</eisbn><eisbn>1424409918</eisbn><abstract>This paper presents a neural fuzzy modeling approach based on hybrid clustering technique to predict a diesel engine's NOx emissions. A hybrid clustering algorithm is provided. Since the combustion process is very complicated, therefore, it is almost impossible to find a simple and accurate first principle model to predict diesel emissions. Black-box models implementing Artificial Intelligent Techniques must be developed. Fuzzy modeling seems to be one of the most suitable approach for modeling diesel emissions with big oscillations and high frequency. Clustering is used with fuzzy modeling approach for determining fuzzy if-then rules, so that a fuzzy network, trained with back propagation, adjusts the centers and widths of the membership function. This paper uses hybrid clustering techniques to build a neural fuzzy model successfully. The results show that the model has very good accuracy in predicting diesel engine's NOx emissions.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.2007.4413857</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 1062-922X |
| ispartof | 2007 IEEE International Conference on Systems, Man and Cybernetics, 2007, p.3609-3614 |
| issn | 1062-922X 2577-1655 |
| language | eng |
| recordid | cdi_ieee_primary_4413857 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial intelligence Artificial neural networks Clustering algorithms clustering techniques Combustion Computational fluid dynamics Design engineering diesel engine Diesel engines emissions Frequency Fuzzy neural networks membership function neural fuzzy network Predictive models |
| title | Combined hybrid clustering techniques and neural fuzzy networks to predict diesel engine emissions |
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