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JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 76 days
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PUBLISHER

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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2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  1/2017 - 3

 HIGHLY CITED PAPER 

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. See more information about  TORUN, M. on SCOPUS See more information about  TORUN, M. on SCOPUS See more information about TORUN, M. on Web of Science, 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
 
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Download PDF pdficon (1,245 KB) | Citation | Downloads: 900 | Views: 3,883

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.


References | Cited By

Cited-By Clarivate Web of Science

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Cited-By SCOPUS

SCOPUS® Times Cited: 6
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Cited-By CrossRef

[1] Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS), Kıyak, İsmail, Gökmen, Gökhan, Koçyiğit, Gökhan, Karimi-Maleh, Hassan, Journal of Nanomaterials, ISSN 1687-4129, Issue , 2021.
Digital Object Identifier: 10.1155/2021/6681335
[CrossRef]

[2] 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
[CrossRef]

[3] The design and implementation of a software based gain control for EDFAs used in long-haul optical networks, Akcesme, Ozcan, Yucel, Murat, Burunkaya, Mustafa, Optik, ISSN 0030-4026, Issue , 2021.
Digital Object Identifier: 10.1016/j.ijleo.2021.166850
[CrossRef]

[4] Estimating Average Power of Welding Process With Emitted Noises Based on Adaptive Neuro Fuzzy Inference System, Gokmen, Gokhan, Akinci, Tahir Cetin, Kocyigit, Gokhan, Kiyak, Ismail, Akbas, M. Ilhan, IEEE Access, ISSN 2169-3536, Issue , 2023.
Digital Object Identifier: 10.1109/ACCESS.2023.3268525
[CrossRef]

[5] Gain Flattening of Three-stage Erbium-doped Fiber Amplifier Based on GFF+VOA, Yu, Miao, Sun, Dong, 2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE), ISBN 979-8-3503-1469-4, 2023.
Digital Object Identifier: 10.1109/ECICE59523.2023.10383015
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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