Iterative estimation for a non-linear IIR filter with moving average noise by means of the data filtering technique

2015 ◽  
Vol 34 (3) ◽  
pp. 745-764 ◽  
Author(s):  
Yanjiao Wang ◽  
Feng Ding
Keyword(s):  
Moving Average ◽  
Data Filtering ◽  
Iterative Estimation ◽  
Iir Filter ◽  
Non Linear ◽  
Filtering Technique

scholarly journals Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jian Pan ◽  
Hao Ma ◽  
Xiao Jiang ◽  
Wenfang Ding ◽  
Feng Ding
Keyword(s):  
Moving Average ◽  
Measurement Data ◽  
Identification Problem ◽  
Autoregressive Moving Average ◽  
Data Filtering ◽  
Moving Average Process ◽  
Output Data ◽  
Gradient Based ◽  
Filtering Technique ◽  
Negative Gradient

The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input–output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

Parameter Estimation Algorithms for Hammerstein–Wiener Systems With Autoregressive Moving Average Noise

2015 ◽  
Vol 11 (3) ◽  
Author(s):  
Yanjiao Wang ◽  
Feng Ding
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Data Filtering ◽  
Estimation Algorithms ◽  
Gradient Based ◽  
Wiener Systems ◽  
Filtering Technique ◽  
Identification Theory

Hammerstein–Wiener (H–W) systems are a class of typical nonlinear systems. This paper studies the gradient-based parameter estimation algorithms for H–W nonlinear systems based on the multi-innovation identification theory and the data filtering technique. The proposed methods include a generalized extended stochastic gradient (GESG) algorithm, a multi-innovation GESG (MI-GESG) algorithm, a data filtering based GESG (F-GESG) algorithm and a data filtering based MI-GESG algorithm. Finally, the computational efficiency of the proposed algorithms are analyzed and compared. The simulation example verifies the theoretical results.

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