NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine
To meet electronic control technology demand based on cylinder pressure feedback in diesel engine, prediction of cylinder pressure feedback variable based on Radial Basis Function (RBF) neural networks is made. Briefly analyzed disadvantage of curve fitting method by multi-parameter input mapping si...
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| creator | Wang, Jun Zhang, Youtong Xiong, Qinghui Ding, Xiaoliang |
| description | To meet electronic control technology demand based on cylinder pressure feedback in diesel engine, prediction of cylinder pressure feedback variable based on Radial Basis Function (RBF) neural networks is made. Briefly analyzed disadvantage of curve fitting method by multi-parameter input mapping single output, radial basis function neural networks is introduced, faster algorithm of Orthogonal Least Squares (OLS) is adopted to calculate networks. Prediction model of cylinder pressure feedback variable based on radial basis function neural networks is present by using Nitric Oxide (NOx) as example, training time and prediction precision is analyzed, comparing with BP neural networks, verification of prediction result by RBF neural networks is made. Test result is shown that prediction model of cylinder pressure feedback variable based on radial basis function neural networks can meet the requirement of diesel engine. |
| doi_str_mv | 10.1109/ICMTMA.2010.621 |
| format | Conference Proceeding |
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Briefly analyzed disadvantage of curve fitting method by multi-parameter input mapping single output, radial basis function neural networks is introduced, faster algorithm of Orthogonal Least Squares (OLS) is adopted to calculate networks. Prediction model of cylinder pressure feedback variable based on radial basis function neural networks is present by using Nitric Oxide (NOx) as example, training time and prediction precision is analyzed, comparing with BP neural networks, verification of prediction result by RBF neural networks is made. Test result is shown that prediction model of cylinder pressure feedback variable based on radial basis function neural networks can meet the requirement of diesel engine.</description><identifier>ISSN: 2157-1473</identifier><identifier>ISBN: 1424450012</identifier><identifier>ISBN: 9781424450015</identifier><identifier>EISBN: 9781424457397</identifier><identifier>EISBN: 1424457394</identifier><identifier>DOI: 10.1109/ICMTMA.2010.621</identifier><identifier>LCCN: 2009943962</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Curve fitting ; cylinder pressure ; diesel engine ; Diesel engines ; Electric variables control ; Engine cylinders ; Neural networks ; Neurofeedback ; prediction ; Predictive models ; Pressure control ; radial basis function ; Radial basis function networks</subject><ispartof>2010 International Conference on Measuring Technology and Mechatronics Automation, 2010, Vol.2, p.792-795</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/5460165$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5460165$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Zhang, Youtong</creatorcontrib><creatorcontrib>Xiong, Qinghui</creatorcontrib><creatorcontrib>Ding, Xiaoliang</creatorcontrib><title>NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine</title><title>2010 International Conference on Measuring Technology and Mechatronics Automation</title><addtitle>ICMTMA</addtitle><description>To meet electronic control technology demand based on cylinder pressure feedback in diesel engine, prediction of cylinder pressure feedback variable based on Radial Basis Function (RBF) neural networks is made. Briefly analyzed disadvantage of curve fitting method by multi-parameter input mapping single output, radial basis function neural networks is introduced, faster algorithm of Orthogonal Least Squares (OLS) is adopted to calculate networks. Prediction model of cylinder pressure feedback variable based on radial basis function neural networks is present by using Nitric Oxide (NOx) as example, training time and prediction precision is analyzed, comparing with BP neural networks, verification of prediction result by RBF neural networks is made. Test result is shown that prediction model of cylinder pressure feedback variable based on radial basis function neural networks can meet the requirement of diesel engine.</description><subject>Algorithm design and analysis</subject><subject>Curve fitting</subject><subject>cylinder pressure</subject><subject>diesel engine</subject><subject>Diesel engines</subject><subject>Electric variables control</subject><subject>Engine cylinders</subject><subject>Neural networks</subject><subject>Neurofeedback</subject><subject>prediction</subject><subject>Predictive models</subject><subject>Pressure control</subject><subject>radial basis function</subject><subject>Radial basis function networks</subject><issn>2157-1473</issn><isbn>1424450012</isbn><isbn>9781424450015</isbn><isbn>9781424457397</isbn><isbn>1424457394</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjM1OAjEURmuUREDWLtz0BQbvbTvtdAkjKAl_MezJzPTWVMfBTIcoby8GVyffOcnH2D3CGBHs4yJf7VaTsYCz0AKv2MiaDJVQKjXSmms2uAwAFDesLzA1CSoje2wgAKxV0mpxy0YxvgOAxCyzRvTZdr354duWXKi6cGh4eeL5qQ6No_ZPx3hsiU-LSI6f6-t0ztd0bIv6jO770H7w0PCnQJFqPmveQkN3rOeLOtLon0O2m892-Uuy3Dwv8skyCRa6xGvwaSmlk0TWUZFZTQilq6zLtBOoswora73GVFlRSEKPvlROem2cUSCH7OFyG4ho_9WGz6I97VOlAXUqfwG9gVK8</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Wang, Jun</creator><creator>Zhang, Youtong</creator><creator>Xiong, Qinghui</creator><creator>Ding, Xiaoliang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine</title><author>Wang, Jun ; Zhang, Youtong ; Xiong, Qinghui ; Ding, Xiaoliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-f60f5b33d3ee9dea896e10bdc9d86d2168c1c99f615492a3e1f1fb4d3f67d7403</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithm design and analysis</topic><topic>Curve fitting</topic><topic>cylinder pressure</topic><topic>diesel engine</topic><topic>Diesel engines</topic><topic>Electric variables control</topic><topic>Engine cylinders</topic><topic>Neural networks</topic><topic>Neurofeedback</topic><topic>prediction</topic><topic>Predictive models</topic><topic>Pressure control</topic><topic>radial basis function</topic><topic>Radial basis function networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Zhang, Youtong</creatorcontrib><creatorcontrib>Xiong, Qinghui</creatorcontrib><creatorcontrib>Ding, Xiaoliang</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>Wang, Jun</au><au>Zhang, Youtong</au><au>Xiong, Qinghui</au><au>Ding, Xiaoliang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine</atitle><btitle>2010 International Conference on Measuring Technology and Mechatronics Automation</btitle><stitle>ICMTMA</stitle><date>2010-03</date><risdate>2010</risdate><volume>2</volume><spage>792</spage><epage>795</epage><pages>792-795</pages><issn>2157-1473</issn><isbn>1424450012</isbn><isbn>9781424450015</isbn><eisbn>9781424457397</eisbn><eisbn>1424457394</eisbn><abstract>To meet electronic control technology demand based on cylinder pressure feedback in diesel engine, prediction of cylinder pressure feedback variable based on Radial Basis Function (RBF) neural networks is made. Briefly analyzed disadvantage of curve fitting method by multi-parameter input mapping single output, radial basis function neural networks is introduced, faster algorithm of Orthogonal Least Squares (OLS) is adopted to calculate networks. Prediction model of cylinder pressure feedback variable based on radial basis function neural networks is present by using Nitric Oxide (NOx) as example, training time and prediction precision is analyzed, comparing with BP neural networks, verification of prediction result by RBF neural networks is made. Test result is shown that prediction model of cylinder pressure feedback variable based on radial basis function neural networks can meet the requirement of diesel engine.</abstract><pub>IEEE</pub><doi>10.1109/ICMTMA.2010.621</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Curve fitting cylinder pressure diesel engine Diesel engines Electric variables control Engine cylinders Neural networks Neurofeedback prediction Predictive models Pressure control radial basis function Radial basis function networks |
| title | NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine |
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