浙江大学学报(英文版)
浙江大學學報(英文版)
절강대학학보(영문판)
JOURNAL OF ZHEJIANG UNIVERSITY SCIENCE
2002年
5期
543-548
,共6页
苏石川%严兆大%元广杰%曹韵华%周重光
囌石川%嚴兆大%元廣傑%曹韻華%週重光
소석천%엄조대%원엄걸%조운화%주중광
Back-propagation neural network (EBP)%Compound fuel%Emissions%Prediction
This paper presents a method using a large steady-state engine operation data matrix to provide necessary information for successfully training a predictive network, while at the same time eliminating errors produced by the dispersive effects of the emissions measurement system. The steady-state training conditions of compound fuel allow for the correlation of time-averaged in-cylinder combustion variables to the engine-out NOx and HC emissions. The error back-propagation neural network (EBP) is then capable of learning the relationships between these variables and the measured gaseous emissions, and then interpolating between steady-state points in the matrix. This method for NOx and HC has been proved highly successful.
