Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle
Author:
Affiliation:
1. Taizhou Electric Power Conversion and Control Engineering Technology Research Center Taizhou University Taizhou China
2. School of Science Xi'an Jiaotong Liverpool University Suzhou China
Funder
National Natural Science Foundation of China
Publisher
Wiley
Subject
Applied Mathematics,Control and Optimization,Software,Control and Systems Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/oca.2766
Reference89 articles.
1. Decomposition‐based multiinnovation gradient identification algorithms for a special bilinear system based on its input‐output representation
2. Hierarchical recursive generalized extended least squares estimation algorithms for a class of nonlinear stochastic systems with colored noise
3. Parameter estimation with scarce measurements
4. Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems
5. Separable Newton recursive estimation method through system responses based on dynamically discrete measurements with increasing data length;Xu L;Int J Control Autom Syst,2021
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1. Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements;Journal of the Franklin Institute;2023-07
2. Multi‐innovation gradient‐based iterative identification methods for feedback nonlinear systems by using the decomposition technique;International Journal of Robust and Nonlinear Control;2023-06-03
3. Data filtering‐based parameter estimation algorithms for a class of nonlinear systems with colored noises;Optimal Control Applications and Methods;2023-02-28
4. Identification of dual‐rate sampled errors‐in‐variables systems with time delays;Optimal Control Applications and Methods;2023-02-12
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