Nonlinear System Identification Using a Subband Adaptive Volterra Filter

2009 ◽  
Vol 58 (5) ◽  
pp. 1389-1397 ◽  
Author(s):  
T.G. Burton ◽  
R.A. Goubran ◽  
F. Beaucoup
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Volterra Filter

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.

Simulation and Analysis of Nonlinear System Identification Using the LMS Volterra Filter

2011 ◽  
Vol 403-408 ◽  
pp. 3528-3537
Author(s):  
Amrita Rai ◽  
A.K. Kohli
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Convergence Criteria ◽  
Step Size ◽  
Volterra Filter ◽  
Precise Analysis ◽  
Finite Word

Nonlinear system Identification based on Volterra filter are widely used for the nonlinearity identification in various application. A standard algorithm for LMS-Volterra filter for system identification simulation, tested with several convergence criteria is presented in this paper. We analyze the steady-state mean square error (MSE) convergence of the LMS algorithm when random functions are used as reference inputs. In this paper, we make a more precise analysis using the deterministic nature of the reference inputs and their time-variant correlation matrix. Simulations performed under MATLAB show remarkable differences between convergence criteria with various value of the step size. Along with that the least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. The second order LMS Volterra filter with variable step size for system identification are analyzed and comparing the theoretical value with experimental value. Copyright © 2009 IFSA.

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