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JCR Impact Factor: 0.595
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Issues per year: 4
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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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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  1/2017 - 3

Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber Amplifiers

YUCEL, M. See more information about YUCEL, M. on SCOPUS See more information about YUCEL, M. on IEEExplore See more information about YUCEL, M. on Web of Science, CELEBI, F. V. See more information about  CELEBI, F. V. on SCOPUS See more information about  CELEBI, F. V. on SCOPUS See more information about CELEBI, F. V. on Web of Science, TORUN, M., GOKTAS, H. H. See more information about GOKTAS, H. H. on SCOPUS See more information about GOKTAS, H. H. on SCOPUS See more information about GOKTAS, H. H. 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 (1,245 KB) | Citation | Downloads: 283 | Views: 482

Author keywords
fuzzy neural networks, adaptive control, gain control, power control, erbium-doped fiber amplifiers

References keywords
fuzzy(22), optical(16), gain(16), edfa(11), celebi(10), systems(9), inference(9), control(9), anfis(9), adaptive(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 15 - 20
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01003
Web of Science Accession Number: 000396335900003
SCOPUS ID: 85014152564

Abstract
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Erbium-doped fiber amplifiers (EDFA) must have a flat gain profile which is a very important parameter such as wavelength division multiplexing (WDM) and dense WDM (DWDM) applications for long-haul optical communication systems and networks. For this reason, it is crucial to hold a stable signal power per optical channel. For the purpose of overcoming performance decline of optical networks and long-haul optical systems, the gain of the EDFA must be controlled for it to be fixed at a high speed. In this study, due to the signal power attenuation in long-haul fiber optic communication systems and non-equal signal amplification in each channel, an automatic gain controller (AGC) is designed based on the adaptive neuro-fuzzy inference system (ANFIS) for EDFAs. The intelligent gain controller is implemented and the performance of this new electronic control method is demonstrated. The proposed ANFIS-based AGC-EDFA uses the experimental dataset to produce the ANFIS-based sets and the rule base. Laser diode currents are predicted within the accuracy rating over 98 percent with the proposed ANFIS-based system. Upon comparing ANFIS-based AGC-EDFA and experimental results, they were found to be very close and compatible.


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Cited-By CrossRef

[1] New neuro-fuzzy system-based holey polymer fibers drawing process, Mohammed Salim, Omar Nameer, AIP Advances, ISSN 2158-3226, Issue 10, Volume 7, 2017.
Digital Object Identifier: 10.1063/1.4998270
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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