Auxiliary model‐based multi‐innovation recursive identification algorithms for an input nonlinear controlled autoregressive moving average system with variable‐gain nonlinearity

2021 ◽  
Author(s):  
Yamin Fan ◽  
Ximei Liu
Keyword(s):  
Moving Average ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Auxiliary Model ◽  
Variable Gain ◽  
Model Based ◽  
Average System

scholarly journals ROBUST RECURSIVE IDENTIFICATION OF HAMMERSTEIN MODELS BASED ON WEISZFALD ALGORITHM

2020 ◽  
Vol 18 (2) ◽  
pp. 127
Author(s):  
Vojislav Filipović
Keyword(s):  
Robust Statistics ◽  
Moving Average ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Weighted Norm ◽  
Stochastic Disturbance ◽  
Hammerstein Models ◽  
Non Gaussian ◽  
Weighting Coefficients ◽  
Nonlinear Autoregressive

The Hammerstein models can accurately describe a wide variety of nonlinear systems (chemical process, power electronics, electrical drives, sticky control valves). Algorithms of identification depend, among other, on the assumption about the nature of stochastic disturbance. Practical research shows that disturbances, owing the presence of outliers, have a non-Gaussian distribution. In such case it is a common practice to use the robust statistics. In the paper, by analysis of the least favourable probability density, it is shown that the robust (Huber`s) estimation criterion can be presented as a sum of non-overlapping - norm and - norm criteria. By using a Weiszfald algorithm - norm criterion is converted to - norm criterion. So, the weighted - norm criterion is obtained for the identification. The main contributions of the paper are: (i) Presentation of the Huber`s criterion as a sum of - norm and - norm criteria; (ii) Using the Weiszfald algorithm  – norm criterion is converted to a weighted - norm criterion; (iii) Weighted extended least squares in which robustness is included through weighting coefficients are derived for NARMAX (nonlinear autoregressive moving average with exogenous variable) . The illustration of the behaviour of the proposed algorithm is presented through simulations.

Recursive Identification of the Dynamic Behavior in a Distillation Column by Means of Autoregressive Models

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Lakhdar Aggoune ◽  
Yahya Chetouani ◽  
Hammoud Radjeai
Keyword(s):  
Performance Measures ◽  
Distillation Column ◽  
Moving Average ◽  
Information Criterion ◽  
Recursive Identification ◽  
Autoregressive Moving Average ◽  
Model Parameters ◽  
Recursive Algorithms ◽  
Optimal Model ◽  
Armax Model

In this study, an Autoregressive with eXogenous input (ARX) model and an Autoregressive Moving Average with eXogenous input (ARMAX) model are developed to predict the overhead temperature of a distillation column. The model parameters are estimated using the recursive algorithms. In order to select an optimal model for the process, different performance measures, such as Aikeke's Information Criterion (AIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE), are calculated.

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