A Study on Extreme Learning Machine for Gasoline Engine Torque Prediction

• Published in: IEEE access Vol. 8; pp. 104762 - 104774
• Main Authors: Zeng, Weiying, Khalid, Mohammed A. S., Han, Xiaoye, Tjong, Jimi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural network; Artificial neural networks; Biological neural networks; Continuity (mathematics); Data points; Engine tests; Engines; Estimation; extreme learning machine; gasoline engine model; Gasoline engines; Idling; Machine learning; Mathematical model; Model accuracy; Model testing; Modelling; Neural networks; Petroleum; Predictive models; Torque; torque prediction; Weight; weight factor
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3000152

This research presents an extreme learning machine (ELM) based neural network modeling technique for gasoline engine torque prediction. The technique adopts a single-hidden layer feedforward neural network (SLFN) structure which has the potential to approximate any continuous function with high accuracy. To verify the robustness of this technique, over 3300 data points collected from a real-world gasoline engine are used to train, validate, and test the model. These data points cover a wide spectrum of normal engine operating conditions, with the engine speed from 1000 rpm to 4500 rpm, and the engine torque from idle to full load. The experiment results demonstrate that the model can predict the gasoline engine torque with high accuracy. Moreover, this research proposes a weight factor approach to further improve the prediction accuracy of the model in the desired data regions without modifying the input data set. The evaluation shows that the weight factor approach can reduce the overall prediction errors in the regions significantly. This feature is particularly useful in tuning the performance of the model when the significance of the individual data points varies, or when the distribution of the data points is imbalanced. In practice, the modeling approaches presented in this research will help reduce the engine test and verification time.