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, ...)
QUESTION:
What other techniques are there to solve this kind of problems ?
Edit: Just realized the question is already quite old... sorry for digging it out. From the comments I understand, that the original post refers to optimization algorithms, that are (well) suited for system identification problems.
I have made (very) good experience with gradient based methods. For my estimation problems particle swarm didn't really improve the estimation results.
Note that (from my point of view) system identification is much more than solving an optimiation problem. If the optimization is difficult of fails, I'd rather review the available data (and eventually the model structure). It's especially important, that the entire dynamics of interest are excited. Loosely speaking, this boils down to maximising the Fisher information matrix of the parameter estimates.