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Adaptive Neuro-Fuzzy Based Gain Controller for Erbium-Doped Fiber AmplifiersYUCEL, M. , CELEBI, F. V. , TORUN, M., GOKTAS, H. H.
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fuzzy neural networks, adaptive control, gain control, power control, erbium-doped fiber amplifiers
fuzzy(22), optical(16), gain(16), edfa(11), celebi(10), systems(9), inference(9), control(9), anfis(9), adaptive(9)
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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
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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Digital Object Identifier: 10.1063/1.4998270 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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