Section: New Results
Ant colony for symbolic regression
Participants : Guillaume Sandou, Bianca Minodora Heiman.
The identification of systems is a key feature to get representative models and so to design efficient control laws. Numerous methods exist to identify the parameters of nonlinear systems when the global structure of the model is given. The problem appears to be much more difficult when this structure is unknown (symbolic regression). We introduces ant colony optimization (ACO) in solving the problem of non linear systems identification in the case of an unknown structure of the mode  . Numerical results prove the viability of the approach.