Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems

2015 ◽  
Vol 107 ◽  
pp. 327-339 ◽  
Author(s):  
Muhammad Asif Zahoor Raja ◽  
Naveed Ishtiaq Chaudhary
Keyword(s):  
Parameter Estimation ◽  
Identification Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Two Stage

scholarly journals Accurate Parameter Estimation for Unbalanced Three-Phase System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Chen ◽  
Hing Cheung So
Keyword(s):  
Parameter Estimation ◽  
Frequency Estimation ◽  
Nonlinear Least Squares ◽  
Phase System ◽  
Mean Square ◽  
Least Mean Square ◽  
Error Performance ◽  
Control Console ◽  
Three Phase ◽  
And Control

Smart grid is an intelligent power generation and control console in modern electricity networks, where the unbalanced three-phase power system is the commonly used model. Here, parameter estimation for this system is addressed. After converting the three-phase waveforms into a pair of orthogonal signals via theαβ-transformation, the nonlinear least squares (NLS) estimator is developed for accurately finding the frequency, phase, and voltage parameters. The estimator is realized by the Newton-Raphson scheme, whose global convergence is studied in this paper. Computer simulations show that the mean square error performance of NLS method can attain the Cramér-Rao lower bound. Moreover, our proposal provides more accurate frequency estimation when compared with the complex least mean square (CLMS) and augmented CLMS.

scholarly journals Robust object identification in the presence of non-Gaussian interference

2019 ◽  
Vol 2 (93) ◽  
pp. 7-12
Author(s):  
O.G. Rudenko ◽  
О. О. Bessonov ◽  
N. М. Serdyuk ◽  
К. О. Olijnik ◽  
О. S. Romanyuk
Keyword(s):  
Parameter Estimation ◽  
Estimation Error ◽  
Object Identification ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Mean Square ◽  
Asymptotic Values ◽  
Mixing Parameter ◽  
Linear Object ◽  
Non Gaussian

The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square
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