Least Squares-Based Iterative Identification Methods for Linear-in-Parameters Systems Using the Decomposition Technique

By extending the least squares based iterative (LSI) method, this paper presents a decomposition based LSI (D-LSI) algorithm for identifying linear-in-parameters systems and an intervalvarying D-LSI algorithm for handling the identification problems of missing-data systems. The basic idea is to apply the hierarchical identification principle to decompose the original system into two fictitious subsystems, then to derive new iterative algorithms to estimate the parameters of each subsystem. Compared with the LSI algorithm and the interval-varying LSI algorithm, the decomposition based iterative algorithms have less computational load. The numerical simulation results demonstrate that the proposed algorithms work quite well.