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• W1952174976 abstract "The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive fi lters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In this paper, we ad dress this problem by introducing a carefully constructed l1 norm (of the coefficients) penalty in the PNLMS cost function which favors sparsity. This results in certain zero attractor terms in th e PNLMS weight update equation which help in the shrinkage of the coefficients, especially the inactive taps, thereby arrest ing the slowing down of convergence and also producing lesser steady state excess mean square error (EMSE). We also carry out the convergence analysis (in mean) of the proposed algorithm." @default.
• W1952174976 created "2016-06-24" @default.
• W1952174976 creator A5022390145 @default.
• W1952174976 creator A5072872028 @default.
• W1952174976 date "2015-07-10" @default.
• W1952174976 modified "2023-09-27" @default.
• W1952174976 title "Zero Attracting PNLMS Algorithm and Its Convergence in Mean" @default.
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• W1952174976 hasPublicationYear "2015" @default.
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