1. NEURAL NETWORK APPROACHES FOR AUTOMOBILE MPG PREDICTION
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 1

3. Data Description 1 Output: MPG Source: UCI Machine Learning Repository
8 Inputs:
1. cylinder # 2. displacement 3. horsepower 4. weight 5. acceleration 6. year 7. origin 8. 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: 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?