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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  1/2015 - 3

 HIGHLY CITED PAPER 

Sparse FIR Filter Design Based on Simulated Annealing Algorithm

WU, C. See more information about WU, C. on SCOPUS See more information about WU, C. on IEEExplore See more information about WU, C. on Web of Science, XU, X. See more information about  XU, X. on SCOPUS See more information about  XU, X. on SCOPUS See more information about XU, X. on Web of Science, ZHANG, X. See more information about  ZHANG, X. on SCOPUS See more information about  ZHANG, X. on SCOPUS See more information about ZHANG, X. on Web of Science, ZHAO, L. See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on SCOPUS See more information about ZHAO, L. on Web of Science
 
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Download PDF pdficon (806 KB) | Citation | Downloads: 1,019 | Views: 2,599

Author keywords
digital filter design, simulated annealing, sparse FIR filter

References keywords
design(12), signal(10), processing(10), filters(10), systems(8), sparse(8), response(7), filter(7), circuits(7), simulated(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 17 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01003
Web of Science Accession Number: 000352158600003
SCOPUS ID: 84924766096

Abstract
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Design of sparse finite impulse response (FIR) filter is of great importance to reduce the implementation cost. However, design of sparse FIR filter under the prescribed constraints is a highly non-convex problem. Traditional methods generally relax the non-convex design problem to a convex one, which leads the obtained solutions suboptimal. In this paper, the non-convex design problem is modeled as a combinatorial optimization problem and an algorithm based on simulated annealing (SA) is presented to solve it. At each stage of the proposed algorithm, with a fixed sparsity of the filter coefficients, SA is employed for finding the possible sparse pattern of the FIR filter that satisfies the prescribed constraints. Once the design constraints have been satisfied, the sparsity is added by one and the algorithm moves to the next stage. The algorithm successively increases the sparsity of the filter coefficients until no sparser solution could be obtained. The proposed algorithm is evaluated by two sets of examples, and better results can be achieved than other existing algorithms.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


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[CrossRef] [SCOPUS Times Cited 354]


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[CrossRef] [SCOPUS Times Cited 41]


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[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 61]


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[CrossRef] [Web of Science Times Cited 4947] [SCOPUS Times Cited 5851]




References Weight

Web of Science® Citations for all references: 57,436 TCR
SCOPUS® Citations for all references: 71,163 TCR

Web of Science® Average Citations per reference: 1,981 ACR
SCOPUS® Average Citations per reference: 2,454 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-04-10 18:07 in 204 seconds.




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