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RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING

Citation Author(s):
Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz
Submitted by:
Hamed Yazdanpanah
Last updated:
3 March 2017 - 9:25pm
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters Name:
Prof. Jose Carlos M. Bermudez
Paper Code:
SPTM-L3.5

Abstract 

Abstract: 

In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l0-norm Recursive least-Squares (l0-RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l0-norm. In addition, we consider the respective versions of these algorithms in data-selective versions in order to reduce the update rate. Simulation results show similar performance when comparing the proposed algorithms with standard Recursive Least-Squares (RLS) algorithm while the proposed algorithms require lower computational complexity.

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RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING

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