Selecting and Fitting Non-Linear Models to Time-Series from Dynamic Systems - Handling the Overfitting Problem with the Neural Network Package

Jonas Sjoberg

Chalmers University of Technology

Abstract

When nonlinear models are fitted to data it is important to make sure that the data is not overfitted. The reason for this is that many possible model structures, all with many parameters, are fitted to the training data, then there is a high risk that the model picks up the specific disturbancies in the training data rather than the general aspects in the data generating process.

The talk illustrates the problem and gives some general possibilities how this problem can be handled. Especially, the methods supported by the Neural Network package together with general support in Mathematica is illustrated.

The talk partly builds on excerps from the course WEG 330.