Parallel and distributed computational data modeling via Verhaegen and Dewilde´s subspace method

Computational data modelling and state-space realization theory for linear discrete time systems are closely related. Verhaegen and Dewilde´s identification algorithm is an efficient subspace data modelling method. For increasing its computational performance, we propose a parallelization structure for such an algorithm, and present some results for an experiment made on a benchmark we developed