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A study of the normalized LMS method with threshold
Author(s) -
Tanpreeyachaya Jirasak,
Nakanishi Takahiro,
Usuda Tsuyoshi,
Takumi Ichi,
Hata Masayasu
Publication year - 2001
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.1072
Subject(s) - least mean squares filter , convergence (economics) , adaptive filter , mathematics , filter (signal processing) , stability (learning theory) , constant (computer programming) , digital filter , signal (programming language) , finite impulse response , control theory (sociology) , algorithm , computer science , computer vision , programming language , economic growth , control (management) , machine learning , artificial intelligence , economics
In this paper, we study a modified normalized least mean square (NLMS) algorithm for updating coefficients of an adaptive FIR digital filter (ADF). In the modified adaptive algorithm, filter coefficients are updated with the NLMS algorithm for each sample, but the coefficients are frozen when the input signal is smaller than a given threshold (constant). This modified NLMS has been known, but not been analyzed. In this paper, we call the modified NLMS the “conditioned NLMS” (C‐NLMS) and analyze the convergence characteristics. As a result, the optimum threshold value is obtained. The simulation results and theoretical analyses show the effectiveness of the C‐NLMS with the proposed threshold, and a good agreement between both results. The stability of the NLMS algorithm in the presence of a small input signal is improved. The convergence speed of the C‐NLMS ADF under noisy circumstances is also faster than that of the unconditioned ordinary NLMS ADF in the small tap number case. © 2001 Scripta Technica, Electr Eng Jpn, 136(4): 47–57, 2001