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In this paper, we propose three new proportionate-type NLMS algorithms: the water filling algorithm, the feasible water filling algorithm, and the adaptive mu-law proportionate NLMS (MPNLMS) algorithm. The water filling algorithm attempts to choose the optimal gains at each time step. The optimal gains are found by minimizing the mean square error (MSE) at each time with respect to the gains, given...
In this paper, we present a proportionate-type normalized least mean square algorithm which operates by choosing adaptive gains at each time step in a manner designed to maximize the conditional probability that the next-step coefficient estimates reach their optimal values. We compare and show that the performance of the maximum conditional probability density one-step algorithm is superior to the...
Using reasonable approximations we do analytical analysis of properties of the proportionate normalized least mean square algorithm (PNLMS) in the case of white and stationary input signal. This work extends ideas applied to the simplified-PNLMS algorithm to the PNLMS algorithm. In particular, the analysis incorporates the max function employed by the PNLMS algorithm which is not present in the simplified-PNLMS...
To date no theoretical results have been developed to predict the performance of the proportionate normalized least mean square (PNLMS) algorithm or any of its cousin algorithms such as the mu-law PNLMS (MPNLMS), and the e-law PNLMS (EPNLMS). In this paper we develop an analytic approach to predicting the performance of the simplified PNLMS algorithm which is closely related to the PNLMS algorithm...
In this paper, a unified framework for representing proportionate type algorithms is presented. This novel representation enables a systematic approach to the problem of design and analysis of proportionate type algorithms. Within this unified framework, the feasibility of predicting the performance of a stochastic proportionate algorithm by analyzing the performance of its associated deterministic...
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