ONLINE CENTERED NLMS ALGORITHM FOR CONCEPT DRIFT COMPENSATION

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F21%3A43923080" target="_blank" >RIV/60461373:22340/21:43923080 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21220/21:00354756
Result on the web
<a href="http://www.nnw.cz/doi/2021/NNW.2021.31.018.pdf" target="_blank" >http://www.nnw.cz/doi/2021/NNW.2021.31.018.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2021.31.018" target="_blank" >10.14311/NNW.2021.31.018</a>

Result language
angličtina
Original language name
ONLINE CENTERED NLMS ALGORITHM FOR CONCEPT DRIFT COMPENSATION
Original language description
This paper introduces an online centered normalized least mean squares (OC-NLMS) algorithm for linear adaptive finite impulse response (FIR) filters and neural networks. As an extension of the normalized least mean squares (NLMS), the OC-NLMS algorithm features an approach of online input centering according to the introduced filter memory. This key feature can compensate the effect of concept drift in data streams, because such a centering makes the filter independent from the nonzero mean value of signal. This approach is beneficial for applications of adaptive filtering of data with offsets. Furthermore, it can be useful for real-time applications like data stream processing where it is impossible to normalize the measured data with respect to its unknown statistical attributes. The OC-NLMS approach holds superior performance in comparison to the NLMS for data with large offsets and dynamical ranges, due to its input centering feature that deals with the nonzero mean value of the input data. In this paper, the derivation of this algorithm is presented. Several simulation results with artificial and real data are also presented and analysed to demonstrate the capability of the proposed algorithm in comparison with NLMS.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/EF16_019%2F0000826" target="_blank" >EF16_019/0000826: Center of Advanced Aerospace Technology</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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