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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Low-Complexity Feature Stochastic Gradient Algorithm for Block-Lowpass Systems

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Author(s):
Yazdanpanah, Hamed [1] ; Diniz, Paulo S. R. [2] ; Lima, Markus V. S. [2]
Total Authors: 3
Affiliation:
[1] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, BR-05508090 Sao Paulo, SP - Brazil
[2] Univ Fed Rio de Janeiro, COPPE, DEL DEE Poli Program Elect Engn, BR-68504 Rio De Janeiro - Brazil
Total Affiliations: 2
Document type: Journal article
Source: IEEE ACCESS; v. 7, p. 141587-141593, 2019.
Web of Science Citations: 0
Abstract

New approaches have been proposed to detect and exploit sparsity in adaptive systems. However, the sparsity is not always explicit among the system coefficients, thus requiring some tools to reveal it. By means of the so-called feature function, we propose the low-complexity feature stochastic gradient (LF-SG) algorithm to exploit hidden sparsity. The proposed algorithm aims at reducing the computational load of the learning process, as compared to the least-mean-square (LMS) algorithm. We focus on block-lowpass systems, but the proposed approach can easily be adapted to exploit other kinds of features of the unknown system, e.g., highpass and bandpass characteristics. Then, we analyze some properties of the LF-SG algorithm, namely its steady-state mean squared error (MSE), its bias, and the choice of the step-size parameter. Simulation results illustrate the competitive MSE performance of the LF-SG in comparison with the LMS, but the former algorithm requires much fewer multiplication operations to identify lowpass systems. For instance, to identify a measured room impulse response, the LF-SG algorithm realized less than half of the multiplication operations required by the LMS algorithm. (AU)

FAPESP's process: 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery
Grantee:Roberto Marcondes Cesar Junior
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 19/06280-1 - Integration, transformation, dataset augmentation and quality control for intermediate representation
Grantee:Hamed Yazdanpanah
Support Opportunities: Scholarships in Brazil - Post-Doctoral