Abstract
In this paper, a novel algorithm called nonlinear multiple-delay feedback kernel least mean square (NMDF-KLMS) is proposed by introducing a nonlinear multiple-delay into the framework of multikernel adaptive filtering. The proposed algorithm incorporates the nonlinear multiple-delay to enhance the filtering performance in comparison with the kernel adaptive filtering algorithm using linear feedback. Furthermore, for NMDF-KLMS, the theoretical mean-square convergence analyses is also conducted. Simulation results under chaotic time-series prediction and real-world data applications show that NMDF-KLMS achieves a faster convergence rate and superior filtering accuracy.
This work is supported in part by the National Natural Science Foundation of China(Grant no. 62201478 and 61971100), in part by the Southwest University of Science and Technology Doctor Fund (Grant no. 20zx7119), in part by the Sichuan Science and Technology Program (Grant no. 2022YFG0148), and in part by the Heilongjiang Provincial Science and Technology Program (No. 2022ZX01A16).
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Notes
- 1.
NMDF-KLMS-CC stands for NMDF-KLMS sparsified by the CC sparsification method.
- 2.
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Zhao, J., Liu, J., Li, Q., Tang, L., Zhang, H. (2024). Nonlinear Multiple-Delay Feedback Based Kernel Least Mean Square Algorithm. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_10
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