Structural identification

In this paper we propose an original modeling method. Our work can be considered as somewhere between classical modeling 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 analytical model definition 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 model acquisition. 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 kinds of problems.