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

  • José Martínez-MoralesEmail author
  • Héctor Quej-Cosgaya
  • José Lagunas-Jiménez
  • Elvia Palacios-Hernández
  • Jorge Morales-Saldaña
Article
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Abstract

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 NOx in a four stroke, spark ignition (SI) gasoline engine and observed acceptable correlation coefficient (R2) of 0.99978.

ant colony optimization multilayer perceptron artificial neural networks hypervolume engine, emissions 

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • José Martínez-Morales
    • 1
    Email author
  • Héctor Quej-Cosgaya
    • 1
  • José Lagunas-Jiménez
    • 1
  • Elvia Palacios-Hernández
    • 2
  • Jorge Morales-Saldaña
    • 3
  1. 1.Faculty of EngineeringAutonomous University of CampecheCampecheMéxico
  2. 2.Faculty of SciencesAutonomous University of San Luis PotosíSan Luis PotosíMéxico
  3. 3.Faculty of EngineeringAutonomous University of San Luis PotosíSan Luis PotosíMéxico

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