SVM and ANFIS for Prediction of Performance and Exhaust Emissions of a SI Engine with Gasoline–Ethanol Blended Fuels

• Main Authors: Najafi, G., Ghobadian, B., Moosavian, A., Yusaf, T., R., Mamat, M., Kettner, Azmi, W. H.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2016
• Subjects: TJ Mechanical engineering and machinery; TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://umpir.ump.edu.my/id/eprint/11655/1/SVM%20and%20ANFIS%20for%20Prediction%20of%20Performance%20and%20Exhaust%20Emissions%20of%20a%20SI%20Engine%20with%20Gasoline%E2%80%93Ethanol%20Blended%20Fuels.pdf; http://umpir.ump.edu.my/id/eprint/11655/; http://dx.doi.org/10.1016/j.applthermaleng.2015.11.009

This paper studies the use of support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and the exhaust emissions of a spark ignition (SI) engine, which operates on ethanol–gasoline blends of 0%, 5%, 10%, 15% and 20% called E0, E5, E10, E15...