Nonlinear Adaptive Filtering With Kernel Set-Membership Approach

This paper develops nonlinear kernel adaptive filtering algorithms based on the set-membership filtering (SMF) framework. The set-membership-based filtering approach is distinct from the conventional adaptive filtering approaches in that it aims for the filtering error being bounded in magnitude, as...

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Veröffentlicht in:	IEEE transactions on signal processing 2020, Vol.68, p.1515-1528
Hauptverfasser:	Chen, Kewei, Werner, Stefan, Kuh, Anthony, Huang, Yih-Fang
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Adaptive algorithms Adaptive filters Algorithms Computational modeling Computer simulation Computing costs Convergence Dictionaries Error analysis Kernel kernel methods Kernels Machine learning algorithms nonlinear systems Parameter estimation set-membership filtering Signal processing algorithms Technological innovation
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