Least squares based iterative identification for a class of multirate systems

Automatica ◽  
2010 ◽  
Vol 46 (3) ◽  
pp. 549-554 ◽  
Author(s):  
Xinggao Liu ◽  
Jing Lu
Keyword(s):  
Least Squares ◽  
Iterative Identification ◽  
Multirate Systems

Decomposition based least squares iterative estimation algorithm for output error moving average systems

2014 ◽  
Vol 31 (4) ◽  
pp. 709-725 ◽  
Author(s):  
Wenge Zhang
Keyword(s):  
Least Squares ◽  
Design Methodology ◽  
Moving Average ◽  
Estimation Algorithm ◽  
Identification Algorithm ◽  
Iterative Identification ◽  
Iterative Estimation ◽  
Content Type ◽  
Engineering Computation ◽  
Parameter Estimation Algorithm

Purpose – The purpose of this paper is to solve the heavy computational problem of parameter estimation algorithm. Design/methodology/approach – Presents a decomposition least squares based iterative identification algorithm. Findings – Can estimate the parameters for linear or pseudo-linear systems and have lower computational burden. Originality/value – This paper adopts a decomposition technique to solve engineering computation problems and offers a potential and efficient algorithm.

scholarly journals Least-Squares-Based Iterative Identification Algorithm for Wiener Nonlinear Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Lincheng Zhou ◽  
Xiangli Li ◽  
Feng Pan
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Iterative Algorithm ◽  
Identification Problem ◽  
Identification Algorithm ◽  
Separation Principle ◽  
Iterative Identification ◽  
Simulation Results ◽  
Information Vector ◽  
Simplified Form

This paper focuses on the identification problem of Wiener nonlinear systems. The application of the key-term separation principle provides a simplified form of the estimated parameter model. To solve the identification problem of Wiener nonlinear systems with the unmeasurable variables in the information vector, the least-squares-based iterative algorithm is presented by replacing the unmeasurable variables in the information vector with their corresponding iterative estimates. The simulation results indicate that the proposed algorithm is effective.

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