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

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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.

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Refferences

  1. Liu K, Zhu H, Lü J. Cooperative stabilization of a class of lti plants with distributed observers. IEEE Trans Circuits Syst I, 2017, 64: 1891–1902

    Article  MathSciNet  Google Scholar 

  2. Chen Y, Lü J. Delay-induced discrete-time consensus. Automatica, 2017, 85: 356–361

    Article  MathSciNet  MATH  Google Scholar 

  3. Shaghaghi E, Jabbarpour M R, Md Noor R, et al. Adaptive green traffic signal controlling using vehicular communication. Front Inf Technol Electron Eng, 2017, 18: 373–393

    Article  Google Scholar 

  4. Zhang J, Liu Y. Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines. Front Inf Technol Electron Eng, 2017, 18: 272–286

    Article  Google Scholar 

  5. Feng D, Xiao M, Liu Y, et al. Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks. Front Inf Technol Electron Eng, 2016, 17: 1287–1304

    Article  Google Scholar 

  6. Feng J, Liu Z, Wu C, et al. AVE: Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Trans Veh Technol, 2017, 66: 10660–10675

    Article  Google Scholar 

  7. Zhang X, Wu Z, Hu X, et al. Trajectory optimization-based auxiliary power unit control strategy for an extended range electric vehicle. IEEE Trans Veh Technol, 2017, 66: 10866–10874

    Article  Google Scholar 

  8. Pei J Z, Su Y X, Zhang D H. Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm. Sci China Technol Sci, 2017, 60: 425–433

    Article  Google Scholar 

  9. Meseguer J, Toh C, Calafate C, et al. Driving styles: A mobile platform for driving styles and fuel consumption characterization. J Commun Netw, 2017, 19: 162–168

    Article  Google Scholar 

  10. Sakthivel G, Snehitkumar B, Ilangkumaran M. Application of fuzzy logic in internal combustion engines to predict the engine performance. Int J Ambient Energy, 2016, 37: 273–283

    Article  Google Scholar 

  11. Deb M, Majumder P, Majumder A, et al. Application of artificial intelligence (AI) in characterization of the performance-emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimization. Int J Hydrogen Energy, 2016, 41: 14330–14350

    Article  Google Scholar 

  12. He G Z, Xie H, He S J. Overall efficiency optimization of controllable mechanical turbo-compounding system for heavy duty diesel engines. Sci China Technol Sci, 2017, 60: 36–50

    Article  Google Scholar 

  13. Ling X C, Wu F, Yao D W. A reduced combustion kinetic model for the methanol-gasoline blended fuels on SI engines. Sci China Technol Sci, 2016, 59: 81–92

    Article  Google Scholar 

  14. Martinez-Morales J D, Palacios-Hernández E R, Velázquez-Carrillo G A. Artificial neural network based on genetic algorithm for emissions prediction of a SI gasoline engine. J Mech Sci Technol, 2014, 28: 2417–2427

    Article  Google Scholar 

  15. Martinez-Morales J D, Palacios-Hernández E R, Velázquez-Carrillo G A. Modeling engine fuel consumption and NOx with RBF neural network and MOPSO algorithm. Int J Automot Technol, 2015, 16: 1041–1049

    Article  Google Scholar 

  16. Kannan G R, Balasubramanian K R, Anand R. Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel. Int J Automot Technol, 2013, 14: 507–519

    Article  Google Scholar 

  17. Ilangkumaran M, Sakthivel G, Nagarajan G. Artificial neural network approach to predict the engine performance of fish oil biodiesel with diethyl ether using back propagation algorithm. Int J Ambient Energy, 2016, 37: 446–455

    Article  Google Scholar 

  18. Niu X, Yang C, Wang H, et al. Investigation of ANN and SVM based on limited samples for performance and emissions prediction of a CRDI-assisted marine diesel engine. Appl Thermal Eng, 2017, 111: 1353–1364

    Article  Google Scholar 

  19. Shi D H, Wang S H, Pisu P, et al. Modeling and optimal energy management of a power split hybrid electric vehicle. Sci China Technol Sci, 2017, 60: 713–725

    Article  Google Scholar 

  20. Muralidharan K, Vasudevan D. Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel. J Braz Soc Mech Sci Eng, 2015, 37: 915–928

    Article  Google Scholar 

  21. Ahmed T, Lim O. A two stroke free piston engine’s performance and exhaust emission using artificial neural networks. J Mech Sci Technol, 2016, 30: 4747–4755

    Article  Google Scholar 

  22. Rahimi-Gorji M, Ghajar M, Kakaee A H, et al. Modeling of the air conditions effects on the power and fuel consumption of the SI engine using neural networks and regression. J Braz Soc Mech Sci Eng, 2017, 39: 375–384

    Article  Google Scholar 

  23. Channapattana S V, Pawar A A, Kamble P G. Optimisation of operating parameters of DI-CI engine fueled with second generation Biofuel and development of ANN based prediction model. Appl Energy, 2017, 187: 84–95

    Article  Google Scholar 

  24. Karthickeyan V, Balamurugan P, Rohith G, et al. Developing of ANN model for prediction of performance and emission characteristics of VCR engine with orange oil biodiesel blends. J Braz Soc Mech Sci Eng, 2017, 39: 2877–2888

    Article  Google Scholar 

  25. Jiang Y Y, Xiang J W, Li B, et al. A hybrid multiple damages detection method for plate structures. Sci China Technol Sci, 2017, 60: 726–736

    Article  Google Scholar 

  26. Lee M C. Effects of H2/CO/CH4 syngas composition variation on the NOx and CO emission characteristics in a partially-premixed gas turbine combustor. Sci China Technol Sci, 2016, 59: 1804–1813

    Article  Google Scholar 

  27. Hagan M, Demuth H, Beale M. Neural Network Design. Boston: PWS Publising Company, 1996

    Google Scholar 

  28. Demuth H, Beale M. Hagan M. Neural Network Toolbox TM 6 User’s Guide. Natick: The Math Works Inc., 2008

    Google Scholar 

  29. Hagan M T, Menhaj M B. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw, 1994, 5: 989–993

    Article  Google Scholar 

  30. Chiam S, Tan K, Mamun A. Multiobjective Evolutionary Neural Networks for Time Series Forecasting. In: Obayashi S, Deb K, Poloni C, et al, eds. Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, 2007, 4403: 346–360

    Google Scholar 

  31. Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern B, 1996, 26: 29–41

    Article  Google Scholar 

  32. Doerner K, Gutjahr W J, Hartl R F, et al. Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection. Ann Operations Res, 2004, 131: 79–99

    Article  MathSciNet  MATH  Google Scholar 

  33. Coello C, Lamont G, Van D. Evolutionary Algorithms for Solving Multi-Objective Problems. New York: Springer, 2007

    MATH  Google Scholar 

  34. Opricovic S, Tzeng G H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur J Operational Res, 2004, 156: 445–455

    Article  MATH  Google Scholar 

  35. Vapnik V. Statistical Learning Theory. New York: Wiley-Interscience, 1998

    MATH  Google Scholar 

  36. Zitzler E, Thiele L. Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans Evol Computat, 1999, 3: 257–271

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Autonomous University of Campeche, Campeche, 24039, México

    José Martínez-Morales, Héctor Quej-Cosgaya & José Lagunas-Jiménez

  2. Faculty of Sciences, Autonomous University of San Luis Potosí, San Luis Potosí, 78290, México

    Elvia Palacios-Hernández

  3. Faculty of Engineering, Autonomous University of San Luis Potosí, San Luis Potosí, 78290, México

    Jorge Morales-Saldaña

Authors
  1. José Martínez-Morales
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  2. Héctor Quej-Cosgaya
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  3. José Lagunas-Jiménez
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  4. Elvia Palacios-Hernández
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  5. Jorge Morales-Saldaña
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Correspondence to José Martínez-Morales.

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Martínez-Morales, J., Quej-Cosgaya, H., Lagunas-Jiménez, J. et al. Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data. Sci. China Technol. Sci. 62, 1055–1064 (2019). https://doi.org/10.1007/s11431-017-9235-y

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  • DOI: https://doi.org/10.1007/s11431-017-9235-y

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