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A convex combination LMS (least mean square) algorithm based on Krylov subspace transform is proposed in this paper. In this approach, impulse response of the unknown system is firstly transformed into Krylov subspace, in which the system structure is changed into sparse. Then an improved proportionate normalized LMS (IPNLMS) algorithm and a variable tap-length normalized LMS (VTNLMS) algorithm are...
A convex combination of adaptive filters is utilized to improve the performance of a variable tap-length least-mean-square (LMS) algorithm in a low signal-to-noise environment (SNRles0 dB). As shown by our simulations, the adaptation of the tap-length in the variable tap-length LMS algorithm is highly affected by the parameter choice and the noise level. Combination approaches can improve such adaptation...
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