NEURAL NETWORK APPROACHES FOR

AUTOMOBILE MPG PREDICTION

12/14/2010

Xiaofei Sun

University of Wisconsin-Madison

ECE 539 Course Project

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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

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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

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Data Preparation

392 sets of data

Correlation coefficients between I/O were calculated

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Linear Regression

7-way cross validation

Training MSE = 11.12 Tuning MSE = 12.70

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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

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Multi Layer Perceptron

Structure: 7-12-1 feedforward network Log-sigmoid function for hidden layer

Linear function for output layer

Training MSE = 4.03 Test MSE = 5.11

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Conclusions and Future Work

MLP yields better performance than linear regression after fine tuning

Will construct radial basis function network, and compare with MLP

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Any Questions?

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