A fast calculation algorithm for a parameter estimation of autoregressive and moving-average model

1983 ◽  
Vol 66 (11) ◽  
pp. 67-75 ◽  
Author(s):  
Yoshikazu Miyanaga ◽  
Hiroshi Watanabe ◽  
Nobuhiro Miki ◽  
Nobuo Nagai
Keyword(s):  
Parameter Estimation ◽  
Moving Average ◽  
Average Model ◽  
Calculation Algorithm ◽  
Fast Calculation ◽  
Moving Average Model

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.

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