This paper presents a neuro-fuzzy approach for predicting vehicle fuel consumption. The prediction of fuel consumption of a vehicle has become a strategic issue. This is because it is not only related to the problem of the availability of fuel which is getting thinner but also the problem of the environmental impact caused. In this study, the acquisition of the car parameter data was inputted, namely the number of cylinders, displacement, horsepower, weight, acceleration, and model year. The output variable that will be predicted is fuel consumption in miles per gallon (MPG). 'Weight' and 'Year' are chosen as the two best input variables. Training results and predictions are expressed in the three-dimensional input-output surface graph of the best two-input ANFIS model for MPG prediction. The graph shows a nonlinear and monotonic surface, where MPG is predicted to increase with an increase in 'Weight' and a decrease in 'Year'. The results of the RMSE training were 2.767 and the RMSE examination was 2.996. Based on the results of the study showed that the greater the weight of motor vehicles, the greater the amount of fuel needed to travel the same distance.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A Neuro-Fuzzy Approach for Vehicle Fuel Consumption Prediction


    Beteiligte:

    Erscheinungsdatum :

    17.09.2018


    Anmerkungen:

    doi:10.18196/jet.2339
    Journal of Electrical Technology UMY; Vol 2, No 3 (2018); 102-110 ; 2580-6823 ; 2550-1186



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




    Green vehicle routing in urban zones – A neuro-fuzzy approach

    Jovanovic, Aleksandar D. / Pamucar, Dragan S. / Pejcic-Tarle, Snezana | Tema Archiv | 2014


    FUEL CONSUMPTION PREDICTION SYSTEM AND FUEL CONSUMPTION PREDICTION METHOD

    SCHOORS GEERT / HENSHER DAVID | Europäisches Patentamt | 2024

    Freier Zugriff

    Multivalued Adaptive Neuro-Fuzzy Controller for Robot Vehicle

    Garbi, G.P. / Rosado, V.O.G. / Grandinetti, F.J. | British Library Conference Proceedings | 2007


    TRAFFIC FLOW SIMULATION BY NEURO-FUZZY APPROACH

    Seitllari, Aksel | TIBKAT | 2014

    Freier Zugriff