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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| Vydáno v: | Digital signal processing Ročník 156; s. 104790 |
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01.01.2025
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 104790 |
| Author | Xue, Wei Zhang, Yanmin Huang, Chunlong Volvenko, Sergey V. Zhou, Wenwen |
| Author_xml | – sequence: 1 givenname: Wenwen orcidid: 0009-0004-1396-6776 surname: Zhou fullname: Zhou, Wenwen organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China – sequence: 2 givenname: Yanmin surname: Zhang fullname: Zhang, Yanmin organization: Hubei Key Laboratory of Marine Electromagnetic Detection and Control, Wuhan Second Ship Design and Research Institute, Wuhan, 430064, China – sequence: 3 givenname: Chunlong orcidid: 0009-0002-0365-1055 surname: Huang fullname: Huang, Chunlong email: cl.huang@hrbeu.edu.cn organization: Yantai Research Institute of Harbin Engineering University, Yantai, 264000, China – sequence: 4 givenname: Sergey V. surname: Volvenko fullname: Volvenko, Sergey V. organization: Higher School of Applied Physics and Space Technologies, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, 195251, Russia – sequence: 5 givenname: Wei surname: Xue fullname: Xue, Wei organization: College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China |
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| Cites_doi | 10.1016/j.dsp.2023.104159 10.1016/j.engappai.2020.103797 10.1162/089976602317250933 10.1016/j.neunet.2013.11.011 10.1109/TNNLS.2013.2258936 10.1109/TSP.2004.830985 10.1109/LSP.2020.2978408 10.1109/TSMC.2017.2760900 10.1109/72.914517 10.1016/j.dsp.2015.09.015 10.1109/TSP.2007.907881 10.1109/TWC.2014.042314.131432 10.1049/el.2013.3997 10.1016/j.physd.2008.07.006 10.1016/j.anihpb.2006.05.001 10.1090/S0002-9947-1950-0051437-7 10.1016/j.isatra.2021.08.014 10.1109/LSP.2017.2761886 10.1016/j.dsp.2021.103255 |
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| Keywords | Non-Gaussian noise conditions Online vector quantization q-Rényi kernel function Online prediction |
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