A Weighted Gaussian Kernel Least Mean Square Algorithm
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149352" target="_blank" >RIV/00216305:26230/23:PU149352 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00034-023-02337-y" target="_blank" >https://link.springer.com/article/10.1007/s00034-023-02337-y</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00034-023-02337-y" target="_blank" >10.1007/s00034-023-02337-y</a>
Alternative languages
Result language
angličtina
Original language name
A Weighted Gaussian Kernel Least Mean Square Algorithm
Original language description
In this work, a novel weighted kernel least mean square (WKLMS) algorithm is proposed by introducing a weighted Gaussian kernel. The learning behavior of the WKLMS algorithm is studied. Mean square error (MSE) analysis shows that the WKLMS algorithm outperforms both the least mean square (LMS) and KLMS algorithms in terms of transient state as well as steady-state responses. We study the effect of the weighted Gaussian kernel on the associated kernel matrix, its eigenvalue spread and distribution, and show how these parameters affect the convergence behavior of the algorithm. Both of the transient and steady-state mean-square-error (MSE) behaviors of the WKLMS algorithm are studied, and a stability bound is derived. For a non-stationary environment, tracking analysis for a correlated random walk channel is presented. We also prove that the steady-state excess MSE (EMSE) of the WKLMS is Schur convex function of the weight elements in its kernel weight matrix and hence it follows the majorization of the kernel weight elements. This helps to decide which kernel weight matrix can provide better MSE performance. Simulations results are provided to contrast the performance of the proposed WKLMS with those of its counterparts KLMS and LMS algorithms. The derived analytical results of the proposed WKLMS algorithm are also validated via simulations for various step-size values.
Czech name
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Czech description
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Classification
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
20202 - Communication engineering and systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
ISSN
0278-081X
e-ISSN
1531-5878
Volume of the periodical
42
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
5267-5288
UT code for WoS article
000969185100004
EID of the result in the Scopus database
2-s2.0-85152403462