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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 NO
x
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 NO
x
method for NO
x
and HC has been proved highly successful. 相似文献
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