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- W237148650 abstract "Based on a bounded-error assumption, set-membership adaptive filtering (SMAF) offers a viable alternative to traditional adaptive filtering techniques that are aimed to minimize an ensemble (or a time) average of the errors. This chapter presents an overview of the principles of SMAF, its features, and some applications. Highlighting the novel features of SMAF includes data-dependent selective update of the parameter estimates. Simulation experiences have shown that the SMAF algorithms normally use less than 20% of the data for updating parameter estimates while achieving performance comparable to traditional algorithms." @default.
- W237148650 created "2016-06-24" @default.
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- W237148650 date "2003-01-01" @default.
- W237148650 modified "2023-09-26" @default.
- W237148650 title "Set-Membership Adaptive Filtering" @default.
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- W237148650 doi "https://doi.org/10.1007/978-1-4612-0037-6_13" @default.
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