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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 and HC has been proved highly successful.
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. 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 emissions emissions, and then interpolating between steady state points in the matrix. This method for NO x and HC has been proved highly successful.