Recursive Subspace Identification with Fixed Data Size and Its Comparison with Other Recursive Algorithms

Abstract

A recursive subspace identification algorithm is derived in which the size of input and output data matrices is fixed. The proposed method estimates both deterministic and stochastic part of the systems. Efficacy of the algorithm is demonstrated by simulations, comparing the numerical results with that obtained by other methods based on the subspace tracking.