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

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


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  1/2015 - 4

FPGA-Based Embedded System Architecture for Micro-Genetic Algorithms Applied to Parameters Optimization in Motion Control

JAEN-CUELLAR, A. Y. See more information about JAEN-CUELLAR, A. Y. on SCOPUS See more information about JAEN-CUELLAR, A. Y. on IEEExplore See more information about JAEN-CUELLAR, A. Y. on Web of Science, MORALES-VELAZQUEZ, L. See more information about  MORALES-VELAZQUEZ, L. on SCOPUS See more information about  MORALES-VELAZQUEZ, L. on SCOPUS See more information about MORALES-VELAZQUEZ, L. on Web of Science, ROMERO-TRONCOSO, R. See more information about  ROMERO-TRONCOSO, R. on SCOPUS See more information about  ROMERO-TRONCOSO, R. on SCOPUS See more information about ROMERO-TRONCOSO, R. on Web of Science, OSORNIO-RIOS, R. A. See more information about OSORNIO-RIOS, R. A. on SCOPUS See more information about OSORNIO-RIOS, R. A. on SCOPUS See more information about OSORNIO-RIOS, R. A. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,535 KB) | Citation | Downloads: 429 | Views: 1,430

Author keywords
control design, genetic algorithms, field programmable gate arrays, microprocessors, servo systems

References keywords
genetic(22), algorithm(17), optimization(12), systems(9), design(8), controller(8), control(8), applications(7), system(6), implementation(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): 23 - 32
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01004
Web of Science Accession Number: 000352158600004
SCOPUS ID: 84924804553

Abstract
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Full text preview
Meta-heuristic techniques are powerful tools used to find an optimal solution for complex problems to which classical techniques find difficult to solve. The features among all the meta-heuristic techniques are the high amount of computational resources spent on their implementation and the computing effort generated on their execution. For this reason, many works have proposed their use on the base of software methodologies without achieving online or real-time performance. In the present work, two strategies that implement the Genetic Algorithms are presented by using the micro-population concept with the objective of reducing computational resources, increasing the heuristic search speed, and providing simplicity in its design. Both strategies are implemented in hardware architecture; the first, as a software strategy in a proprietary embedded processor, the second, as a hardware co-processor unit. In order to validate the proposed approaches, several tests to optimize a motion controller in a servo system are presented and compared with a classical tuning technique.


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References Weight

Web of Science® Citations for all references: 1,579 TCR
SCOPUS® Citations for all references: 2,266 TCR

Web of Science® Average Citations per reference: 44 ACR
SCOPUS® Average Citations per reference: 63 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 2017-09-26 15:07 in 198 seconds.




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