Quantized kernel recursive q-Rényi-like algorithm

This paper introduces the kernel recursive q-Rényi-like (KRqRL) algorithm, based on the q-Rényi kernel function and the kernel recursive least squares (KRLS) algorithm. To reduce the computational complexity and memory requirements of the KRqRL algorithm, an online vector quantization (VQ) method is...

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Veröffentlicht in:	Digital signal processing Jg. 156; S. 104790
Hauptverfasser:	Zhou, Wenwen, Zhang, Yanmin, Huang, Chunlong, Volvenko, Sergey V., Xue, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.01.2025
Schlagworte:	Non-Gaussian noise conditions Online prediction Online vector quantization q-Rényi kernel function Non-Gaussian noise conditions Online vector quantization q-Rényi kernel function Online prediction
ISSN:	1051-2004
Online-Zugang:	Volltext
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Zusammenfassung:	This paper introduces the kernel recursive q-Rényi-like (KRqRL) algorithm, based on the q-Rényi kernel function and the kernel recursive least squares (KRLS) algorithm. To reduce the computational complexity and memory requirements of the KRqRL algorithm, an online vector quantization (VQ) method is employed to quantize the network size to a codebook size, resulting in the quantized KRqRL (QKRqRL) algorithm. This paper provides a detailed analysis of the convergence and computational complexity of the QKRqRL algorithm. In the simulation experiments, the network size of each algorithm is reduced to 25% of its original size. The performance of the QKRqRL algorithm is evaluated in terms of convergence speed, prediction error, and computation time under non-Gaussian noise conditions. Finally, the QKRqRL algorithm is further validated using sunspot data, demonstrating its superior stability and online prediction performance.
ISSN:	1051-2004
DOI:	10.1016/j.dsp.2024.104790