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Proposes hierarchical iterative methods for parameter estimation in autoregressive models, indicating broader applications in stochastic systems.
By means of the auxiliary model identification idea and the multi‐innovation identification theory, this paper investigates iterative parameter identification methods for autoregressive output‐error models, and proposed an auxiliary model (hierarchical) gradient‐based iterative (GI) algorithm, an auxiliary model (hierarchical) least‐squares‐based iterative (LSI) algorithm, an auxiliary model (hierarchical) multi‐innovation GI algorithm, an auxiliary model (hierarchical) multi‐innovation LSI algorithm by using the hierarchical identification principle. The proposed hierarchical iterative identification methods can be extended to other linear and nonlinear scalar and multivariable stochastic systems with colored noises.
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Ding et al. (2026) studied this question.
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