Hierarchical Newton iterative parameter estimation of a class of input nonlinear systems based on the key term separation principle
type: Item
instance of: scholarly article
title: Hierarchical Newton iterative parameter estimation of a class of input nonlinear systems based on the key term separation principle
zbMATH Open document ID: 1407.93378
DOI: 10.1155/2018/7234147
author: Cheng Wang
author: Kaicheng Li
author: Shuai Su
published in: Complexity
publication date: 2019-02-19T00:00:00Z
review text: Summary: This paper investigates the identification problem for a class of input nonlinear systems whose disturbance is in the form of the moving average model. In order to improve the computation complexity, the key term separation principle is introduced to avoid the redundant parameter estimation. Based on the decomposition technique, a hierarchical Newton iterative identification method combining the key term separation principle is proposed for enhancing the estimation accuracy and handling the computational load with the presence of the high dimensional matrices. In the identification procedure, the unknown internal items or vectors are replaced with their iterative estimates. The effectiveness of the proposed identification methods is shown via a numerical simulation example.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93E12
zbMATH DE Number: 7024997
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/7234147
OpenAlex ID: W2898242373
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description: scientific article; zbMATH DE number 7024997