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Hierarchical Iterative Parameter Estimation for Multivariable Systems Based on the Coupled Identification Model
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DOI：10.1002/acs.70110.png)
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Abstract
En 中文
The partially-coupled information vector system refers to a multivariable system in which some of the information (inputs) among sub-systems are coupled, while the rest of the information (outputs) are uncoupled (independent). After parameterizing such a multivariable system, we obtain a partially-coupled information vector identification model. Based on this coupled identification model, we propose corresponding iterative parameter identification methods, including hierarchical gradient-based iterative algorithms, hierarchical least-squares-based iterative algorithms, hierarchical multi-innovation gradient-based iterative algorithms, and hierarchical multi-innovation least-squares-based iterative algorithms, etc. These iterative parameter identification methods can be extended to other linear and nonlinear multivariate stochastic systems with colored noise.
Keywords:
coupling identification
hierarchical identification
iterative identification
multi-innovation identification
parameter estimation
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