Abstract
This research studied the use of Artificial Neural Network (ANN) using feed-forward back-propagation model to optimize and predict the performance of a motorcycle fuel injection systems of gasoline. The parameters such as speed, throttle position, ignition timing and injection timing is used as the input parameters. While the parameters of fuel consumption and engine torque is used as the output layer. Lavenberg-Marquardt model type with train function tanh sigmoid and 25 neurons number is used to generate the target value and the desired output. Variation of ignition timing as optimization variable in a wide range of speed and throttle position is used in experimental tests. ANN is used to investigate the prediction of performance motorcycle engines and compared with the test results. Results showed that the operation of ANN in predicting engine performance is very good. From the test results obtained a smooth contour MAP compared to the initial state. The prediction result and performance test show a good correlation in small error value of training and test that is regression with range 0.98-0.99, mean relative error with range 0.1315-0.4281% and the root mean square error with range 0.2422-0.9754%. This study shows that the feed-forward back propagation on ANN model can be used to predict accurately the performance of a motorcycle engine injection system.
Highlights
The development of automotive technology at the present is very rapidly marked by using injection system (FI)
The results show that there is a good correlation between the results of experiments with Artificial Neural Network (ANN) prediction
The standart data is based on state running on the dynamometer which is connected to the scanner to read the ignition timing and data acquisition to measure the engine's torque
Summary
The development of automotive technology at the present is very rapidly marked by using injection system (FI). We will investigate the ability of artificial neural network using ANN feed-forward back propagation model with Levenberg-Marquardt function 25 number of neurons model to optimize and predict the performance of a motorcycle fuel injection systems of gasoline on a variety of engine rotation (speed) and opening valve (throttle position). In this case the engine performance parameters as output parameters is fuel consumption and engine torque. The results show that there is a good correlation between the results of experiments with ANN prediction
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