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.

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.

scholarly journals Recursive Least-Squares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiangli Li ◽  
Lincheng Zhou ◽  
Ruifeng Ding ◽  
Jie Sheng
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Identification Problem ◽  
Nonuniform Sampling ◽  
Recursive Least Squares ◽  
Separation Principle ◽  
Output Data ◽  
Auxiliary Model ◽  
Information Vector ◽  
Identification Model

This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling. Using the key-term separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period. To estimate parameters of the presented model, an auxiliary model-based recursive least-squares algorithm is derived by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates. The simulation results show the effectiveness of the proposed algorithm.

scholarly journals Hierarchical Newton Iterative Parameter Estimation of a Class of Input Nonlinear Systems Based on the Key Term Separation Principle

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Cheng Wang ◽  
Kaicheng Li ◽  
Shuai Su
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Moving Average ◽  
Identification Problem ◽  
Estimation Accuracy ◽  
Separation Principle ◽  
Iterative Identification ◽  
Average Model ◽  
Identification Procedure ◽  
Moving Average Model

This paper investigates the identification problem for a class of input nonlinear systems whose disturbance is in the form of the moving average model. In order to improve the computation complexity, the key term separation principle is introduced to avoid the redundant parameter estimation. Based on the decomposition technique, a hierarchical Newton iterative identification method combining the key term separation principle is proposed for enhancing the estimation accuracy and handling the computational load with the presence of the high dimensional matrices. In the identification procedure, the unknown internal items or vectors are replaced with their iterative estimates. The effectiveness of the proposed identification methods is shown via a numerical simulation example.

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.

A hierarchical least squares identification algorithm for Hammerstein nonlinear systems using the key term separation

2018 ◽  
Vol 355 (8) ◽  
pp. 3737-3752 ◽  
Author(s):  
Feng Ding ◽  
Huibo Chen ◽  
Ling Xu ◽  
Jiyang Dai ◽  
Qishen Li ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Least Squares ◽  
Identification Algorithm
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