Kernel Recursive Least Squares Algorithm Based on the Nystr }}}}m Method With k-Means Sampling

The kernel recursive least squares (KRLS) algorithm is used to improve the convergence rate and filtering accuracy of kernel adaptive filters (KAFs) in the Gaussian noise case. However, the linear growing network size in KRLS poses a huge amount of time and storage consumption. To address this issue...

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Veröffentlicht in:	IEEE signal processing letters Jg. 27; S. 361 - 365
Hauptverfasser:	Zhang, Tao, Wang, Shiyuan, Huang, Xuewei, Jia, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 2020
Schlagworte:	<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> k</tex-math> </inline-formula>-means sampling Approximation algorithms Eigenvalues and eigenfunctions Kernel Kernel adaptive filters kernel recursive least squares Nonlinear systems Nystr<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> {\rm {{ \ddot{\bf{o}}}}}</tex-math> </inline-formula>m method Prediction algorithms Sampling methods Signal processing algorithms
ISSN:	1070-9908, 1558-2361
Online-Zugang:	Volltext
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