C++ Neural Networks and Fuzzy Logic: Preface

Neural Nets versus Regression Analysis

Leorey Marquez et al. compared neural network modeling with standard regression analysis. The authors used

a feedforward backpropagation network with a structure of 1−6−1. They used three functional forms found in

regression analysis:

1.  Y = B0 + B1 X + e

2.  Y = B0 + B1 log(X) + e

3.  Y = B0 + B1/X + e

For each of these forms, 100 pairs of (x,y) data were generated for this “true” model.

Now the neural network was trained on these 100 pairs of data. An additional 100 data points were generated

by the network to test the forecasting ability of the network. The results showed that the neural network

achieved a MAPE within 0.6% of the true model, which is a very good result. The neural network model

approximated the linear model best. An experiment was also done with intentional mis−specification of some

data points. The neural network model did well in these cases also, but comparatively worse for the reciprocal

model case.