Abstract
The purpose of this paper is to introduce and partially develop new iterative methods for identification of large scale systems. Tne proposed identification algorithms are defined in the prediction-correction estimation framework using locally defined extended Kalman filters. The overall system identification is based on iterative estimation scnemos which resemble classical Jacobi and Gauss-Seidel approaches for solving linear algebraic equations. Prior to identification, the overall system is decomposed by applying a Srapn-theoretic approach. The decompositions are conducive to parallel processing. The adventase of such an approach is a possibility to implement the resulting algorithms on multiple processor systems.
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