Abstract
We present a novel affine projection algorithm (APA) which dynamically selects input vectors in order to improve convergence performance. The optimum selection of the input vectors is derived by the largest decrease of the mean-square deviation. The experimental results show that the proposed algorithm has a fast convergence speed and a small steady-state error compared to the conventional APA. In addition, the proposed algorithm retains a low overall computational complexity.
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