Parameter estimation for nonlinear Volterra systems by using the multi-innovation identification theory and tensor decomposition

2021 ◽  
Author(s):  
Yanjiao Wang ◽  
Shihua Tang ◽  
Xiaobo Gu
Keyword(s):  
Parameter Estimation ◽  
Tensor Decomposition ◽  
Volterra Systems ◽  
Identification Theory

scholarly journals Signal parameter estimation through hierarchical conjugate gradient least squares applied to tensor decomposition

ETRI Journal ◽  
2020 ◽  
Vol 42 (6) ◽  
pp. 922-931
Author(s):  
Long Liu ◽  
Ling Wang ◽  
Jian Xie ◽  
Yuexian Wang ◽  
Zhaolin Zhang
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Conjugate Gradient ◽  
Tensor Decomposition ◽  
Signal Parameter

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.

Weighted Combination of Prior and Sample Information and Parameter Estimation

Optimization ◽  
1976 ◽  
Vol 7 (5) ◽  
pp. 665-672
Author(s):  
H. Burke ◽  
C. Hennig ◽  
W H. Schmidt
Keyword(s):  
Parameter Estimation ◽  
Sample Information ◽  
Weighted Combination

Sequential accuracy in parameter estimation for population correlation coefficients.

2019 ◽  
Vol 24 (4) ◽  
pp. 492-515 ◽  
Author(s):  
Ken Kelley ◽  
Francis Bilson Darku ◽  
Bhargab Chattopadhyay
Keyword(s):  
Parameter Estimation ◽  
Correlation Coefficients ◽  
Population Correlation
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