Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data

作     者：MARTINEZ-MORALES Jose QUEJ-COSGAYA Hector LAGUNAS-JIMENEZ Jose PALACIOS-HERNANDEZ Elvia MORALES-SALDANA Jorge 

作者机构：Faculty of Engineering Autonomous University of Campeche Campeche 24039 Mexico Faculty of Sciences Autonomous University of San Luis Potosi San Luis Potosi 78290 Mexico Faculty of Engineering Autonomous University of San Luis Potosi San Luis Potosi 78290 Mexico 

基　　金：supported by the National Council for Science and Technology of Mexico CONACYT(Grant No.45765) 

出 版 物：《Science China(Technological Sciences)》 (中国科学（技术科学英文版）)

年 卷 期：2019年第62卷第6期

页      码：1055-1064页

摘      要：A multilayer perceptron(MLP) artificial neural network(ANN) model has been optimized by the multi-objective ant colony optimization(MOACO) algorithm, which uses three objective functions. A sensitivity analysis to choose MOACO parameter values is carried out by calculating hypervolume metric, and the proposed approach adopts the Vlsekriterijumska Optimizacija I Kompromisno Resenje(VIKOR) decision method to choose final compromised solution on the Pareto front obtained from MOACO. As a result, we used the MLP-MOACO developed model to estimate the value of engine emissions of NOxin a four stroke, spark ignition(SI) gasoline engine and observed acceptable correlation coefficient(R^2) of 0.99978.

主 题 词：ant colony optimization multilayer perceptron artificial neural networks hypervolume engine emissions 

学科分类：08[工学] 0802[工学-机械学] 

核心收录：

D　O　I：10.1007/s11431-017-9235-y

馆 藏 号：203671022...