1. Estimation of car gas consumption in city cycle with ANN Introduction  An ANN based approach to estimation of car fuel consumption  Multi Layer Perceptron as a choice of the art  Different discrete and continuous features  Prediction without extensive trials on the road  Could be used as first guess

2. Estimation of car gas consumption in city cycle with ANN Data Description  Seven features used: Horsepower, Weight, Number of Cylinders etc.  Some data with unknown values  Wide variation of cars, 398 samples available  Equally distributed from different manufacturers  Quite old, from early ‘80s

3. Estimation of car gas consumption in city cycle with ANN Data Preprocessing  Feature dimension statistical values differ very much  Removing name of car and values with unknown parameters  Normalization of each feature dimension  Create data sets for training and final testing

4. Estimation of car gas consumption in city cycle with ANN MLP Development I  Estimation of underlying physical model difficult  How complex and how “much” nonlinear?  One hidden layer versus several hidden layers  Cross-validation as best way to find optimal configurations  Five parameters for variation

5. Estimation of car gas consumption in city cycle with ANN MLP Development II  Each cross validation performed several times with same parameters to get a meaningful average  Learning rate and epoch size most important  Tables to evaluate best settings  No complete automation, two-step evaluation  Several comparable best configurations

6. Estimation of car gas consumption in city cycle with ANN Comparison to Base Case  Best result from final training still 30-40% worse than base case  No improvement achieved with two hidden layers  Results still good for first estimation  Understanding of model not sufficient enough

7. Estimation of car gas consumption in city cycle with ANN Conclusion  Use of MLP delivered satisfactory results, but not better than base case  Using different activation function could bring improvement  Without some prior knowledge of physical model hard to see what features are more important than others  Other ANN like radial basis functions possible