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- W4386484619 abstract "Different types of exponential functional link artificial neural network (EFLN) filters have been proposed for nonlinear active noise control (NANC) systems, but they still suffer from high computational complexity. To address this issue and further improve steady-state performance, this paper presents a collaborative recursive generalized exponential functional link artificial neural network nonlinear filter based on optional parameter entry method (CR-GEFLN-OPEM). A collaborative filtered-error least mean square (CFELMS) algorithm is also proposed. Simulations and an actual noise control trial demonstrate the improved steady-state performance and low computational complexity of the proposed filter in NANC systems." @default.
- W4386484619 created "2023-09-07" @default.
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- W4386484619 date "2023-10-01" @default.
- W4386484619 modified "2023-09-29" @default.
- W4386484619 title "A collaborative recursive generalized exponential functional link artificial neural network nonlinear filter based on optional parameter entry method" @default.
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- W4386484619 doi "https://doi.org/10.1016/j.dsp.2023.104216" @default.
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