Artificial neural network based on genetic algorithm for emissions prediction of a SI gasoline engine

This paper proposes a hybrid learning of artificial neural network (ANN) with the nondominated sorting genetic algorithm-II (NSGAII) to improve accuracy in order to predict the exhaust emissions of a four stroke spark ignition (SI) engine. In the proposed approach, the genetic algorithm (GA) determines initial weights of local linear model tree (LOLIMOT) neural networks. A multi-objective optimization problem is determined. A sensitivity analysis is performed on NSGA-II parameters in order to provide better solutions along the optimal Pareto front. Then, a fuzzy decision maker and the technique for order preference by similarity to ideal solution (TOPSIS) are employed to select compromised solutions among the obtained Pareto solutions. The LOLIMOT-GA responses are compared with the provided by radial basis function (RBF) and multilayer perceptron (MLP) neural networks in terms of correlation coefficient R 2. © 2014 The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg.

Artificial neural networks; Exhaust emissions of engine; Genetic algorithm; Multi-objective optimization Algorithms; Engines; Genetic algorithms; Ignition; Neural networks; Radial basis function networks; Correlation coefficient; Exhaust emission; Local linear model tree (LOLIMOT); Multi-objective optimization problem; Multilayer perceptron neural networks; Non-dominated sorting genetic algorithm - ii; Radial Basis Function(RBF); Technique for order preference by similarity to ideal solutions; Multiobjective optimization

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