NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine

摘要：

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 multiparameter 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.

关键词： prediction cylinder pressure radial basis function neural networks diesel engine

作者: WANG Jun ZHANG Youtong XIONG Qinghui DING Xiaoliang

作者单位: Department of Mechanical Engineering ,Academy of Armored Forces Engineering ,Beijing, 100072, China School of Mechanical and Vehicular Engineering, Beijing Institute of Technology, Beijing, 100081,China

会议类型: 国际会议

会议名称: 2010 International Conference on Measuring Technology and Mechatronics Automation(ICMTMA 2010)(2010年检测技术与机电自动化国际会议)

会议地点: 长沙

会议语种:英文

页码: 1917-1920

在线出版日期: 2010-03-13（万方平台首次上网日期，不代表论文的发表时间）