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This paper develops a hierarchical extended stochastic gradient identification algorithms for MIMO ARMAX-like systems to deal with colored noises based on the hierarchical identification principle. The convergence performance of such algorithms is studied in detail; in particular, conditions for parameter estimation errors to converge to zero are established, which include persistent excitation of...
Difficulty of parameter identification for general stochastic systems is there exist both unknown noise-free outputs (i.e., true outputs) and unmeasurable noise terms in the information vector. Using the auxiliary model identification technique to establish an auxiliary model based on the measurable input-output data of the system and replacing the unknown noise-free outputs in the information vector...
This paper derives an identification model for a class of stochastic systems with colored noises. The information vector in the identification model contains both unknown noise-free outputs (i.e., true outputs) and unmeasurable noise terms, this is difficulty of identification. This paper establishes an auxiliary model by using the measurable information of the system and replaces the unknown noise-free...
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