Abstract
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.
Highlights
Creating accurate gasoline engine torque models is a challenging task
We present an approach to create a singlehidden layer feedforward neural network based regression model using extreme learning machine (ELM) that can predict the output torque of a gasoline engine
If the root mean square error (RMSE) of the validation data set is within 5% of the range of RMSE of the training data, the regulation factor k would be deemed as acceptable and the model would be evaluated with the test data set
Summary
Creating accurate gasoline engine torque models is a challenging task. Firstly, the gasoline engine is a complex system, whose operation often involves multidisciplinary interactions and contains a lot of transient states. The neural networks all use a backpropagation algorithm to train the weight of each neuron This algorithm may not be easy enough to implement, especially for the non-experts, due to the disadvantages such as difficulty in determining proper learning rate, getting trapped in local minima, prone to over-training, and very time-consuming for most of the applications [22]. These neural network models are typically created and tested with about 80 to 130 data points. We present an approach to create a singlehidden layer feedforward neural network based regression model using ELM that can predict the output torque of a gasoline engine. The target of the SLFN approximation turns into finding out the appropriate estimations of β, wand b, which can minimize the cost of the estimation E, where
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
