scholarly journals Efficient implementation of the third order RLS adaptive Volterra filter

2006 ◽  
Vol 19 (1) ◽  
pp. 133-141 ◽  
Author(s):  
Georgeta Budura ◽  
C. Botoca
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Nonlinear System Identification ◽  
Third Order ◽  
Volterra Filter ◽  
The Third ◽  
Nonlinear Adaptive Filtering ◽  
Made In

Nonlinear adaptive filtering techniques are widely used for the nonlinearities identification in many applications. This paper proposes a new implementation of the third order RLS Volterra filter based on the decomposition of the input vector. Its performances are evaluated in a typical nonlinear system identification application. Different degrees of nonlinearity for the nonlinear system are considered. Comparations, based on the adaptive filter error, are made in all cases with a linear identifier. The experimental results show that the proposed nonlinear identifier has better performances than the linear one.

scholarly journals Robustness Analysis of the Estimators for the Nonlinear System Identification

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 834
Author(s):  
Wiktor Jakowluk ◽  
Karol Godlewski
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Adaptive Filtering ◽  
Nonlinear System Identification ◽  
Robustness Analysis ◽  
Estimation Methods ◽  
Function Evaluation ◽  
Model Parameters ◽  
Maximum Information ◽  
Plant Model

The main objective of the system identification is to deliver maximum information about the system dynamics, while still ensuring an acceptable cost of the identification experiment. The focus of such an idea is to design an appropriate experiment so that the departure from normal working conditions during the identification task is minimized. In this paper, the adaptive filtering (AF) scheme for plant model parameter estimation is proposed. The experimental results are obtained using the nonlinear interacting water tanks system. The adaptive filtering is expressed by minimizing the error between the system and the plant model outputs subject to the white noise signal affecting system output. This measurement error is quantified by the comparison of Minimum Error Entropy Renyi (MEER), Least Entropy Like (LEL), Least Squares (LS), and Least Absolute Deviation (LAD) estimators, respectively. The plant model parameters were obtained using sequential quadratic programming (SQP) algorithm. The robustness tests for the double-tank water system parameter estimators are performed using the ellipsoidal confidence regions. The effectiveness analysis for the above-mentioned estimators relies on the total number of iterations and the number of function evaluation comparisons. The main contribution of this paper is the evaluation of different estimation methods for the nonlinear system identification using various excitation signals. The proposed empirical study is illustrated by the numerical examples, and the simulation results are discussed.

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