Abstract
The proposed algorithm in this paper is the quantized minimum kernel risk hyperbolic secant adaptive filtering algorithm, which offers a simplified approach to enhancing the performance and stability of kernel adaptive filtering in non-Gaussian noise environments. The algorithm features a newly developed minimum kernel risk hyperbolic secant cost function, which harnesses the hyperbolic secant function’s strengths to diminish outlier impacts and expedite convergence. In addition, its convex kernel risk-sensitive loss surface facilitates swift and accurate filtering via gradient-based methods, thus ensuring outlier robustness. This method could effectively manage network size and reduce computational complexity by incorporating vector quantization for inputting spatial data. Simulation tests in Mackey–Glass time series prediction and nonlinear system identification have indicated that the minimum kernel hyperbolic secant adaptive filtering algorithm and its quantized variant excel in terms of convergence speed, robustness, and computational efficiency.
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All data generated or analyzed during this study are included in this article. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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Funding
This work was supported by science and technology program of Gansu Province(No.21JR7RA120), the National Natural Science Foundation of China(No.61862041).
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HY proposed the innovative points of the paper, while HZ and HY co-wrote the main manuscript text. QY and LJ were responsible for drawing Figs. 1, 23 and the algorithm flowchart. HY and LZ were responsible for drawing Tables 1, 2 and 3. All authors reviewed the manuscript.
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Yibo, H., Zhiling, H., Yuanlian, H. et al. A quantized minimum kernel risk hyperbolic secant adaptive filtering algorithm. SIViP 18, 4291–4301 (2024). https://doi.org/10.1007/s11760-024-03072-w
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DOI: https://doi.org/10.1007/s11760-024-03072-w