PROJECT TITLE :
Bias-compensated affine-projection-like algorithms with noisy input
A replacement class of bias-compensated affine-projection-like (APL) algorithms is proposed, in that a bias-compensation vector is derived to eliminate the bias caused by the noisy input. Additionally, a replacement estimation technique for the input noise variance is proposed that will not need the input–output noise variance ratio earlier. Simulations in a system identification context show that the proposed algorithms achieve important enhancements in steady-state misalignment as compared with the traditional APL algorithms.
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