A New Method for 2-D Moving Average Model Parameter Estimation

2014 ◽  
Vol 60 (5) ◽  
pp. 364-372 ◽  
Author(s):  
Mahdi Zeinali ◽  
Masoud Shafiee
Keyword(s):  
Parameter Estimation ◽  
Moving Average ◽  
New Method ◽  
Average Model ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Moving Average Model

A NEW METHOD FOR OBTAINING THE AUTOCOVARIANCE OF AN ARMA MODEL: AN EXACT FORM SOLUTION

1998 ◽  
Vol 14 (5) ◽  
pp. 622-640 ◽  
Author(s):  
M. Karanasos
Keyword(s):  
Moving Average ◽  
Closed Form Solution ◽  
Arma Model ◽  
Form Solution ◽  
New Method ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Model Redundancy

In this article we present a new method for computing the theoretical autocovariance function of an autoregressive moving average model. The importance of our theorem is that it yields two interesting results: First, a closed-form solution is derived in terms of the roots of the autoregressive polynomial and the parameters of the moving average part. Second, a sufficient condition for the lack of model redundancy is obtained.

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.

Sign in / Sign up

Export Citation Format

Share Document