期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3199679
关键词
Kernel; Complex networks; Convergence; Adaptive systems; Upper bound; Learning systems; Mathematical models; Complex network; kernel adaptive filter; least mean square (LMS); recursive least square (RLS)
类别
资金
- National Key Research and Development Program of China [2018YFB1402600]
- National Natural Science Foundation of China [61933007, 61976013, 12171124, 61873148, 61873082]
- Alexander von Humboldt Foundation of Germany
This paper focuses on the problem of kernel adaptive filtering for a complex network. It proposes a coupled KLMS algorithm and a coupled KRLS algorithm to improve the filtering performance. The validity and convergence of the algorithms are demonstrated through theoretical analysis and simulation.
This brief is concerned with the problem of kernel adaptive filtering for a complex network. First, a coupled kernel least mean square (KLMS) algorithm is developed for each node to uncover its nonlinear measurement function by using a series of input-output data. Subsequently, an upper bound is derived for the step-size of the coupled KLMS algorithm to guarantee the mean square convergence. It is shown that the upper bound is dependent on the coupling weights of the complex network. Especially, an optimal step size is obtained to achieve the fastest convergence speed and a suboptimal step size is presented for the purpose of practical implementations. Besides, a coupled kernel recursive least square (KRLS) algorithm is further proposed to improve the filtering performance. Finally, simulations are provided to verify the validity of the theoretical results.
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