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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Bibliographic Details
Published in:Digital signal processing Vol. 156; p. 104790
Main Authors: Zhou, Wenwen, Zhang, Yanmin, Huang, Chunlong, Volvenko, Sergey V., Xue, Wei
Format: Journal Article
Language:English
Published: Elsevier Inc 01.01.2025
Subjects:
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 Access:Get full text
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Summary: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