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A Partially Decoupled RLS Algorithm for Volterra Filters
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BibTeX
@MISC{Griffith_apartially,
author = {David W. Griffith and Jr. and Gonzalo R. Arce},
title = {A Partially Decoupled RLS Algorithm for Volterra Filters},
year = {}
}
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Abstract
In this paper we consider a partially decoupled variation of the RLS algorithm is based on a constrained optimization of the cumulative filter error using the higherorder sets of filter weights to improve on the performance of the lower-order weight sets, whose values are already established. From this constrained optimization, a recursive algorithm is developed whose form closely resembles the classic RLS algorithm, but with some structural differences. It is shown that these differences lead to a reduction in computational complexity and in increase in the rate of convergence. Submitted to the IEEE Transactions on Signal Processing EDICS Paper Category: SP 2.7.3 (Non-linear Filters) Permission to publish this abstract separately is granted 1 Introduction Nonlinear digital filtering techniques have been applied to a wide variety of problems in signal estimation and equalization for which linear filters are not adequate. Examples of such situations include modeling nonlinear dynamic...
Keyphrases
volterra filter partially decoupled rls algorithm non-linear filter signal processing edics paper category computational complexity rls algorithm structural difference lower-order weight set decoupled variation nonlinear dynamic linear filter filter weight higherorder set wide variety recursive algorithm cumulative filter error signal estimation constrained optimization classic rls algorithm introduction nonlinear digital filtering technique ieee transaction
