.png){kind=icon}
.png){kind=icon}
Return
Identification of nonlinear errors-in-variables models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2003.06.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The paper is about a generalization of a classical eigenvalue-decomposition method originally developed for errors-in-variables linear system identification to handle an important class of nonlinear problems. A number of examples are presented to call the attention to the most critical part of the procedure turning the identification problem to a generalized eigenvalue-eigenvector calculation problem with symmetrical matrices. The elaborated method generates consistent parameter estimation. Simulation results demonstrate the effectiveness of the proposed algorithm. (C) 2003 Elsevier Ltd. All rights reserved.
Keywords:
errors-in-variables model
nonlinear system identification
eigenvalue-eigenvector decomposition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.