Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle-Reference-Cited by-同舟云学术

Multi‐innovation gradient parameter estimation for multivariable systems based on the maximum likelihood principle

Published:2021-08-09 Issue:1 Volume:43 Page:106-122
ISSN:0143-2087
Container-title:Optimal Control Applications and Methods
language:en
Short-container-title:Optim Control Appl Methods

Author:

Xia Huafeng¹^ORCID,Xu Sheng¹,Zhou Cheng¹,Chen Feiyan²

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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