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Adaptive Filtering Based on Minimum Error Entropy Conjugate Gradient

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Abstract

Based on the criterion of minimum error entropy, this paper proposes a novel conjugate gradient algorithm, called MEE-CG. This algorithm has robust performance under non-Gaussian interference. Theoretical analysis and experimental results demonstrate that the proposed algorithm displays more robust performance than the conventional conjugate gradient methods on the basis of the mean square error and the maximum correntropy criterion. Compared with the stochastic gradient minimum error entropy algorithm and the recursive minimum error entropy algorithm, the proposed algorithm provides a trade-off between computational complexity and convergence speed.

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Acknowledgements

The authors would like to thank all the reviewers who participated in the review.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 61371182, 61971100 and 51975107.

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Authors and Affiliations

  1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Guoliang Li, Qi Sun, Gang Wang, Xinyue Yang & Linqiang Ouyang

  2. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Ying Zhang

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  1. Guoliang Li
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  2. Qi Sun
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  3. Ying Zhang
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  4. Gang Wang
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Correspondence to Ying Zhang.

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Li, G., Sun, Q., Zhang, Y. et al. Adaptive Filtering Based on Minimum Error Entropy Conjugate Gradient. Circuits Syst Signal Process (2024). https://doi.org/10.1007/s00034-024-02654-w

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