Maximum likelihood least squares iterative identification algorithm for hammerstein output error moving average systems

2016 ◽  
Author(s):  
Junhong Li ◽  
Weixing Zheng ◽  
Yi Yang ◽  
Qing Zhang ◽  
Chen Li
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Moving Average ◽  
Identification Algorithm ◽  
Iterative Identification ◽  
Output Error

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 The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jiling Ding
Keyword(s):  
Least Squares ◽  
Identification Problem ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
Input Output ◽  
Iterative Identification ◽  
Hierarchical Identification ◽  
Output Error ◽  
Gradient Based ◽  
Simulation Results

This paper considers the identification problem of multi-input-output-error autoregressive systems. A hierarchical gradient based iterative (H-GI) algorithm and a hierarchical least squares based iterative (H-LSI) algorithm are presented by using the hierarchical identification principle. A gradient based iterative (GI) algorithm and a least squares based iterative (LSI) algorithm are presented for comparison. The simulation results indicate that the H-LSI algorithm can obtain more accurate parameter estimates than the LSI algorithm, and the H-GI algorithm converges faster than the GI algorithm.

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

2020 ◽  
Vol 30 (15) ◽  
pp. 6262-6280 ◽  
Author(s):  
Mengting Chen ◽  
Feng Ding ◽  
Rongming Lin ◽  
Teng Yong Ng ◽  
Yanliang Zhang ◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Iterative Methods ◽  
Output Error
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