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Least squares-based iterative identification methods for linear-in-parameters systems using the decomposition technique. (English) Zbl 1345.93171

Summary: 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 interval-varying 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 sub-systems and then to derive new iterative algorithms to estimate the parameters of each sub-system. 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.

MSC:

93E24 Least squares and related methods for stochastic control systems
93E10 Estimation and detection in stochastic control theory
93E12 Identification in stochastic control theory
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