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Generalized complex kernel least-mean-square algorithm with adaptive kernel widths

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Abstract

A novel variable kernel width generalized complex-valued least mean-square (VKW-GCKLMS) algorithm aims to optimize kernel width in online way to tackle with the problem that the performance of nonlinear kernel algorithms with fixed values of kernel widths is degraded by inappropriate choice of kernel widths. The proposed VKW-GCKLMS algorithm is able to continuously optimize values of kernel widths by using stochastic gradient algorithm in the task of complex-valued nonlinear filtering. Numerical simulations illustrate that the VKW-GCKLMS algorithm is capable of guiding values of kernel widths of different kernels toward those that can achieve the optimal filtering performances. In addition, it is also shown that initial values of kernel widths do not have evident effect on the filtering performance of the VKW-GCKLMS algorithm, which demonstrates the high effectiveness of the VKW-GCKLMS algorithm.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgments

This work was supported by National Key R &D Program of China (2022YFE0198900), National Natural Science Foundation of China (61771430) and Basic Public Welfare Research Project of Zhejiang Province in China (LGG18F030002).

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  1. Zezhen Huang and Hua Gao have contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science, Zhejiang University of Technology, Hangzhou, 310014, Zhejiang, China

    Wei Huang, Zezhen Huang & Hua Gao

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  1. Wei Huang
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Correspondence to Hua Gao.

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Huang, W., Huang, Z. & Gao, H. Generalized complex kernel least-mean-square algorithm with adaptive kernel widths. Neural Comput & Applic 35, 6423–6434 (2023). https://doi.org/10.1007/s00521-022-08022-6

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