NOx prediction by cylinder pressure based on RBF neural network in diesel engine

Jun Wang*, Youtong Zhang, Qinghui Xiong, Xiaoliang Ding

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Pages792-795
Number of pages4
DOIs
Publication statusPublished - 2010
EventInternational Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010 - Changsha, China
Duration: 13 Mar 201014 Mar 2010

Publication series

Name2010 International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Volume2

Conference

ConferenceInternational Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2010
Country/TerritoryChina
CityChangsha
Period13/03/1014/03/10

Keywords

  • Cylinder pressure
  • Diesel engine
  • Neural networks
  • Prediction
  • Radial basis function

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