On bias compensated least squares method for noisy input-output system identification

2003 ◽  
Author(s):  
Li-Juan Jia ◽  
M. Ikenoue ◽  
Chun-Zhi Jin ◽  
K. Wada
Keyword(s):  
System Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Input Output ◽  
Noisy Input ◽  
Output System

IDENTIFICATION OF NOISY INPUT-OUTPUT SYSTEM USING BIAS-COMPENSATED LEAST-SQUARES METHOD

2005 ◽  
Vol 38 (1) ◽  
pp. 803-808 ◽  
Author(s):  
Masato Ikenoue ◽  
Shunshoku Kanae ◽  
Zi-Jiang Yang ◽  
Kiyoshi Wada
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Input Output ◽  
Noisy Input ◽  
Output System

A Modified Algorithm for the Least Squares Identification

1983 ◽  
Vol 105 (1) ◽  
pp. 50-52
Author(s):  
C. Batur
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Mechanical Systems ◽  
Model Structure ◽  
Input Output ◽  
Unknown Parameters ◽  
Estimation Errors ◽  
Usual Practice ◽  
Unknown Disturbances ◽  
Modified Algorithm

To identify the dynamics of mechanical systems, the usual practice is to assume a certain model structure and try to estimate the unknown parameters of this model on the basis of input output observations. For mechanical systems operating under noisy industrial conditions, the number of unknowns of the problem exceeds the number of equations available. It is then inevitable that certain assumptions must be made on the unknown disturbances. This paper assumes that the only reliable feature of the disturbance is its independence of input. This yields a set of assumptions in excess of the minimal requirements and an endeavor has been made to exploit this excess to minimize the parameter estimation errors. Th resulting algorithm is similar to that of the Two Stage Least Squares method [1].

Sign in / Sign up

Export Citation Format

Share Document