(South Valley University, Egypt)(2) * Nouby M. Ghazaly
(1) Faculty of Engineering, South Valley University, Qena-83523, Egypt. 2) Technical College, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq)(3) Ahmed N. Shmroukh
(1) Faculty of Engineering, South Valley University, Qena-83523, Egypt. 2) Faculty of Industry and Energy Technology, New Cairo Technological University, Cairo 11835, Egypt)(4) Mostafa Abouelsoud
(South Valley University, Egypt)*corresponding author
AbstractCurrently, artificial neural networks (ANNs) and support vector machines (SVMs) are the most common applications of machine learning approaches. In this study, a comparative study of ANN and SVM is presented to evaluate the performance of each model in predicting the brake power (BP) of GX35-OHC 4-stroke, air-cooled, single cylinder gasoline engine with E15 (15% ethanol + 85% gasoline) fuel. Two models are compared based on experimental dataset that has single output (BP) and five inputs, engine speed (S), engine torque (T), intake air temperature (Tair), intake air flow (Qair), and fuel consumption (ṁ). The samples were split into three sets: Training set (70%), Validation set (15%), and the Test set (15%) based on 60 samples. The results are compared through different graphs such as target vs actual values, regression plots, histograms of prediction errors, residual plots, learning curves, and error distributions. The results showed that SVM model is indicated to have lower RMSE (0.0044) and higher EVS (0.9953), while ANN is shown to have lower value of MAPE (1.51%). These results have significant implications for the use of ANN and SVM models in real-world applications that need gradual comprehensibility and model generalization. In addition, work done with the models outlined above should try and test them in other engines and operating conditions to demonstrate the model’s and performance. KeywordsArtificial Neural Network; Engine Performance; SI Engines; Support Vector Machine; Machine Learning
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DOIhttps://doi.org/10.31763/ijrcs.v4i3.1429 |
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