On convergence proofs in system identification – a general principle using ideas from learning theory

It is shown that learning theory offers convergence analysis tools that are useful in system identification problems. They allow analysis in a parameter-free context, which elevates the analysis from parameter sets to model sets and from parameter identification to model identification. When a parameterization is eventually introduced, this leads to alternative assumptions on the parameterization and parameter set. Moreover, structural identification can be analyzed within the same framework. Another advantage is that the proofs are technically and conceptually simple.