Abstract
This work presents recent results obtained in the field of recur sive parameter identification for discrete time MIMO systems utilizing Least Squares Derived techniques. The purpose is twofold: to extend existing techniques by considering a general ARMA model for the noise and to analyze in which situations the algorithms obtained can be partitioned to allow a parallel implementation. The algorithms considered are: Extended Matrix, Generalized Least Squares, Approximated Maximum Likelihood and a new partitioned algorithm obtained from the Extended Matrix technique. The main results show that in the ARMA situation the first and last algorithms lead directly to a parallel implementation. The other two algorithms require the utilization of special canonical forms which, in general, will imply an in crease in the number of parameters to be estimated. Nevertheless the more accurate approximation utilized by the Approximated Maximum Likelihood tech nique compensates for this fact.
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