NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION

NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION

NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION

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Presentation Transcript

1. ECE 539 Course Project NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION 12/14/2010 Xiaofei Sun University of Wisconsin-Madison

2. Motivations • Nowadays, fuel economy becomes a great concern of the governments and drivers • MPG varies with vehicle specs and conditions • Database available online only accounts for different models • Large amount of data required • Build NN models to predict the MPG based on given specs and conditions • MLP • RBF 1/8

3. Data Description • Source: UCI Machine Learning Repository • http://archive.ics.uci.edu/ml/datasets/Auto+MPG • 8 Inputs: • 1. cylinder #2. displacement3. horsepower4. weight5. acceleration6. year7. origin8. manufacturer • 1 Output: MPG

4. Data Preparation • 392 sets of data • Correlation coefficients between I/O were calculated

5. Linear Regression • 7-way cross validation Training MSE = 11.12 Tuning MSE = 12.70

6. Multi Layer Perceptron • MATLAB Neural Network Toolbox Used • Learning algorithms: • Gradient descent with momentum • Scaled conjugate gradient • Levenberg-Marquardt  • Datasets were randomly divided into three subsets: • 60% for training • 20% for validation (early stopping) • 20% for testing

7. Multi Layer Perceptron • Structure: 7-12-1 feedforward network • Log-sigmoid function for hidden layer • Linear function for output layer Test MSE = 5.11 Training MSE = 4.03

8. Conclusions and Future Work • MLP yields better performance than linear regression after fine tuning • Will construct radial basis function network, and compare with MLP

9. ? Any Questions?