Abstract
This paper presents a composite learning (CL) architecture. The CL can make use of the a dvantages of both direct and indirect learning. By an appropriate threshold, the indirect learning can be switched into direct learning. In order to further improve the performance of direct learning, an improved variable step size LMS algorithm is proposed. The proposed algorithm has the lower norma lized mean-square error (NMSE) with low computation complexity. The outstanding performance is confirmed by simulation results.
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