Online estimation of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process based on the colored noise data from recording <i>2–81731-A-10.wav</i> as a function of sample number <i>k</i> obtained using the proposed correlation method and the recursive pseudolinear regression approach.

Online estimation of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process based on the colored noise data from recording <i>2–81731-A-10.wav</i> as a function of sample number <i>k</i> obtained using the proposed correlation method and the recursive pseudolinear regression approach.

<p>Online estimation of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process based on the colored noise data from recording <i>2–81731-A-10.wav</i> as a function of sample number <i>...

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Bibliographic Details
Main Author: Martin Dodek (20623832) (author)
Other Authors: Eva Miklovičová (20623835) (author)
Published: 2025
Subjects:
Biophysics
Neuroscience
Biotechnology
Infectious Diseases
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
unlike conventional methods
numerical experiments confirm
new recursive formula
multiple symmetrical solutions
moving average process
method &# 8217
levenberg &# 8211
integrating exponential forgetting
enable parameter adaptation
linear equations solvable
online parameter estimation
efficient online estimation
sample autocorrelation function
autocorrelation function
nonlinear equations
offline estimation
estimation complexity
xlink ">
unique technique
performance compared
paper introduces
older samples
novelty lies
novel approach
notably reduced
necessary conditions
model parameters
marquardt algorithms
key finding
efficiently find
colored noise
backward substitution
approach uses
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