Convergence Analysis of Processing Cost Reduction Method of NLMS Algorithm with Correlated Gaussian Data
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概要
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Reduction of the complexity of the NLMS algorithm has received attention in the area of adaptive filtering. A processing cost reduction method, in which the component of the weight vector is updated when the absolute value of the sample is greater than or equal to an arbitrary threshold level, has been proposed. The convergence analysis of the processing cost reduction method with white Gaussian data has been derived. However, a convergence analysis of this method with correlated Gaussian data, which is important for an actual application, is not studied. In this paper, we derive the convergence characteristics of the processing cost reduction method with correlated Gaussian data. From the analytical results, it is shown that the range of the gain constant to insure convergence is independent of the correlation of input samples. Also, it is shown that the misadjustment is independent of the correlation of input samples. Moreover, it is shown that the convergence rate is a function of the threshold level and the eigenvalues of the covariance matrix of input samples as well as the gain constant.
- 社団法人電子情報通信学会の論文
- 1996-07-25
著者
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TAKAHASHI Kiyoshi
Department of Medicine, National Minami-Okayama Hospital
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Kuroyanagi Noriyoshi
Department Of Information Networks Tokyo Engineering University
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Takahashi Kiyoshi
Department Of Information Networks Tokyo Engineering University
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Takahashi Kiyoshi
Department Of Agronomy Faculty Of Agriculture Tohoku University
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