I would like to ask for suggestions regarding system identification with known model structure, but without known parameters.
The model is a model of a physical system, it can be assumed that it is stable, non-linear and dynamic. The input/output data are available with and without noise.
While the model structure is known, the values of the parameters are unknown. The models have up to 10 parameters (just to give you a hint about dimensionality).
Options I am aware of to solve these problems:
- quasi-random search algorithms (Particle Swarm, Genetic Algorithm, ...)
- gradient based methods (backpropagation through time, forward perturbation, ...)
What other techniques are there to solve this kind of problems ?