Mean values of the estimated of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process as a function of sample number <i>k</i> obtained assuming λ = 0.99998 using the proposed correlation method and the RPLR across <i>M</i> = 100 realizations of input noise sequence.

Mean values of the estimated of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process as a function of sample number <i>k</i> obtained assuming λ = 0.99998 using the proposed correlation method and the RPLR across <i>M</i> = 100 realizations of input noise sequence.

<p>Mean values of the estimated of the coefficients <i>g</i><sub><i>i</i></sub>(<i>k</i>) of the non-stationary moving average process as a function of sample number <i>k</i> obtained assuming λ = 0.99998 using the proposed correlatio...

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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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