Abstract
A novel adaptive LMS (least mean square) algorithm is presented in which the algorithm can update the filter coefficients along both the horizontal and the vertical directions on a 2-D plane. Both the conventional algorithm and the new 2-D LMS algorithm are applied to the identification of unknown 2-D systems with stationary or nonstationary characteristics. The learning curves and the mean square errors show that the new 2-D LMS adaptive algorithm is particularly suitable for processing 2-D nonstationary signals. However, it converges slowly for stationary inputs.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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