Feasibility Study Of Neural Network Approach In Engine Management System In S.I. Engine

As neural network approach has shown very encouraging results in different fields of engineering. The present work is an attempt to use the NN approach in engine management system to control the air fuel ratio and ignition timing with highest possible accuracy to meet the more and more stringent emission regulation. A feed-forward Neural network with one hidden layer has been used to predict the pulse width & ignition timing. Optimum number of hidden neurons and learning rate were observed to be 13 and 0.7 respectively. After training, the network was validated for 180 sample data and further cross-validated for about 400 data samples. The neural network output results show that the maximum absolute error for pulse width is 0.016 during validation and 0.050 during cross-validation. The analysis of the neural network output shows that the neural network has learn the input output data relations really well and is capable to predict the pulse width and ignition timing in the decided domain.