Bayesian Programming

Probability as an extension of logic


Structural identification

PhD thesis of Remis Balaniuk (1996)

In this thesis we propose an original method to models acquisition: the structural identification.

Our work can be situated somewhere between classical modélisation methods and learning based methods.

We show that in the scope of a particular but quite general functional class it is possible to automatically choose the best equation form to represent a physical process avoiding the hard work of model characterization that the designer should do.

Our method uses an experimental protocol where the parameters identification is limited to one entry dimension problems reducing the amount of data required by the modélisation.

The models generated by our method can be easily differentiated, corrected and reused. The method can be particularly useful in robotics where the functional form the method hands can be easily found in many kind of problems.


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