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José D. Martínez-Morales,Elvia R. Palacios-Hernández,Gerardo A. Velázquez-Carrillo 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.6
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, thegenetic algorithm (GA) determines initial weights of local linear model tree (LOLIMOT) neural networks. A multi-objective optimizationproblem is determined. A sensitivity analysis is performed on NSGA-II parameters in order to provide better solutions along theoptimal Pareto front. Then, a fuzzy decision maker and the technique for order preference by similarity to ideal solution (TOPSIS) areemployed to select compromised solutions among the obtained Pareto solutions. The LOLIMOT-GA responses are compared with theprovided by radial basis function (RBF) and multilayer perceptron (MLP) neural networks in terms of correlation coefficient R².