scholarly journals Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems

2012 ◽  
Vol 63 (5) ◽  
pp. 975-984 ◽  
Author(s):  
Guoyu Yao ◽  
Ruifeng Ding
Keyword(s):  
Least Squares ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Identification Algorithm ◽  
Iterative Identification ◽  
Two Stage ◽  
Two Stage Least Squares

scholarly journals Least Squares Based and Two-Stage Least Squares Based Iterative Estimation Algorithms for H-FIR-MA Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhenwei Shi ◽  
Zhicheng Ji
Keyword(s):  
Least Squares ◽  
Iterative Algorithm ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Moving Average ◽  
Two Stage ◽  
Iterative Estimation ◽  
Estimation Algorithms ◽  
Two Stage Least Squares

This paper studies the identification of Hammerstein finite impulse response moving average (H-FIR-MA for short) systems. A new two-stage least squares iterative algorithm is developed to identify the parameters of the H-FIR-MA systems. The simulation cases indicate the efficiency of the proposed algorithms.

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 Decomposition Least-Squares-Based Iterative Identification Algorithms for Multivariable Equation-Error Autoregressive Moving Average Systems

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 609 ◽  
Author(s):  
Lijuan Wan ◽  
Ximei Liu ◽  
Feng Ding ◽  
Chunping Chen
Keyword(s):  
Least Squares ◽  
Moving Average ◽  
Identification Problem ◽  
Autoregressive Moving Average ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
Hierarchical Identification ◽  
Iterative Search ◽  
Time Varying Parameters ◽  
Equation Error

This paper is concerned with the identification problem for multivariable equation-error systems whose disturbance is an autoregressive moving average process. By means of the hierarchical identification principle and the iterative search, a hierarchical least-squares-based iterative (HLSI) identification algorithm is derived and a least-squares-based iterative (LSI) identification algorithm is given for comparison. Furthermore, a hierarchical multi-innovation least-squares-based iterative (HMILSI) identification algorithm is proposed using the multi-innovation theory. Compared with the LSI algorithm, the HLSI algorithm has smaller computational burden and can give more accurate parameter estimates and the HMILSI algorithm can track time-varying parameters. Finally, a simulation example is provided to verify the effectiveness of the proposed algorithms.

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