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- W2139164015 abstract "We develop a generalized RLS (G-RLS) algorithm described by a state-space model through some modification of the procedure for Kalman filter derivation. It is shown that the G-RLS algorithm reduces to the conventional RLS when the state transition matrix is an identity matrix, and that the G-RLS algorithm without exponential weighting and Kalman filtering become identical when the state model is an unforced dynamical model. The G-RLS algorithm does not require model statistics, and can be implemented once the forgetting factor is chosen. The performances of the G-RLS and Kalman filtering are compared through computer simulation. Specifically, they are applied to the derivation of variable loop gains of a digital phase-locked loop (DPLL). The results indicate that the G-RLS algorithm can act like the Kalman filter if its forgetting factor is properly chosen." @default.
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- W2139164015 date "2002-11-27" @default.
- W2139164015 modified "2023-09-27" @default.
- W2139164015 title "Generalization of exponentially weighted RLS algorithm based on a state-space model" @default.
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- W2139164015 doi "https://doi.org/10.1109/iscas.1998.694442" @default.
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