Modeling engine fuel consumption and NOx with RBF neural network and MOPSO algorithm

In this study, artificial neural network (ANN) modeling is used to predict the fuel consumption and NOx emission of a four stroke spark ignition (SI) engine. Calibration engineers frequently want to know the responses of an engine for the entire range of operating conditions in order to change engine control parameters in the electronic control unit (ECU), to improve performance and reduce emissions. However, testing the engine for the complete range of operating conditions is a very time and labor consuming task. As alternative, ANN is used in order to predict fuel consumption and NOx emission. In the proposed approach, the multi-objective particle swarm optimization (MOPSO) is used to determine weights of radial basis function (RBF) neural networks. The goal is to minimize performance criteria as root mean square error (RMSE) and model complexity. A sensitivity analysis is performed on MOPSO parameters in order to provide better solutions along the optimal Pareto front. In order to select a compromised solution among the obtained Pareto solutions, a fuzzy decision maker is employed. The correlation coefficient R2 is used to compare the engine responses with the obtained by the proposed approach. © 2015, The Korean Society of Automotive Engineers and Springer-Verlag Berlin Heidelberg.

Artificial neural network; Exhaust emissions; Multi-objective particle swarm optimization; Spark ignition engine Algorithms; Complex networks; Control systems; Decision making; Engines; Fuels; Internal combustion engines; Mean square error; Neural networks; Nitrogen oxides; Particle swarm optimization (PSO); Radial basis function networks; Sensitivity analysis; Artificial neural network models; Correlation coefficient; Electronic control units; Engine control parameters; Exhaust emission; Multi objective particle swarm optimization; Radial basis function neural networks; Root mean square errors; Multiobjective optimization

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