Partial-Update L∞-Norm Adaptive Filtering Algorithm with Sparse Updates
نویسندگان
چکیده
This paper provides a partial-update normalized sign least-mean square (NSLMS) algorithm with sparse updates. The proposed algorithm reduces the computational complexity compared with the conventional L∞-norm adaptive filtering algorithms by decreasing the frequency of updating the filter coefficients and updating only a part of the filter coefficients. And we develop a mean square analysis to present the convergence of the proposed algorithm. Experimental results show that the proposed algorithm has the good convergence performance with greatly reduced computational complexity.
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