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


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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: 134 | Views: 179

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

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  «-- Click to see who has cited this paper

[1] Y. Huang, E. Ip, M. F. Huang, B. Zhu, P. N. Ji, Y. Shao, D. W. Peckham, R. Lingle, Y. Aono, T. Tajima, T. Wang, "10×456 Gb/s DP-16QAM transmission over 8×100 km of ULAF using coherent detection with a 30 GHz analog-to-digital converter", in Proc. of OECC, pp. 1-2, Japan, 2010.

[2] X. Zhou, J. Yu, M. F. Huang, Y. Shao, T. Wang, L. Nelson, P. Magill, M. Birk, P. I. Borel, D. W. Peckham, R. Lingle, "64 Tb/s (640×107 Gb/s) PDM-36QAM transmission over 320 km using both pre- and post-transmission digital equalization", in Proc. of IEEE/OSA OFC/NFOEC, pp. 1-3, CA, No. PDPB9, San Diego, 2010.

[3] N. Ankitkumar, P. N. Patel, Jason P. Ji, T. W. Jue, "Routing, wavelength assignment, and spectrum allocation algorithms in transparent flexible optical WDM networks", Optical Switching and Networking vol. 9, issue 3, pp. 191-204, 2012.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 36]


[4] H. J. Chen, X. L. Yang, "Gain flattened erbium-doped fiber amplifier using simple equalizing film", International Journal of Infrared and Millimeter Waves, vol. 20, issue 12, pp. 2107-2121, 1999.
[CrossRef] [Web of Science Times Cited 3]


[5] S. Dung, J.C. Chi, S. Wen, "Gain Flattening of Erbium doped Fibre Amplifier Using Fibre Bragg Gratings", Electronics Letters, vol. 34, pp. 555-556, 1998.
[CrossRef] [Web of Science Times Cited 9]


[6] C. L. Zhao, H. Y. Tam, B. Guan, X. Dong, P. K. A. Wai, X. Dong, "Optical automatic gain control of EDFA using two oscillating lasers in a single feedback loop", Optics Communications, vol. 225, pp. 157-162, 2003.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 27]


[7] M. Yucel, H. H. Goktas, "Design of gain flattened ultra-wide band hybrid optical amplifier", J. Fac. Eng. Archit. Gazi Univ., vol. 22, pp. 863-868, 2007.

[8] M. Yucel, Z. Aslan, "The noise figure and gain improvement of double-pass C- band EDFA", Microw. Opt. Technol. Lett., vol. 55, pp. 2525-2528, 2013.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


[9] M. Yucel, "Fuzzy logic-based automatic gain controller for EDFA", Microwave and Optical Technology Letters, vol. 53, pp. 2703-2705, 2011.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]


[10] Y. Ben-Ezra, M. Haridim, B. I. Lembrikov, "All-Optical AGC of EDFA based on SOA", IEEE J Quantum Electron., vol. 42, pp. 1209-1214, 2006.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 11]


[11] H. Nakaji, Y. Nakai, M. Shigematsu, M. Nishimura, "Superior high-speed automatic gain controlled erbium-doped fiber amplifiers", Opt Fiber Technol., vol. 9, pp. 25-35, 2003.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 19]


[12] C. L. Zhao, H. Y. Tam, B. O. Guan, X. Dong, P. K. A. Wai, X. Dong, "Optical automatic gain control of EDFA using two oscillating lasers in a single feedback loop", Optics Comm., vol. 225, pp. 157-162, 2003.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 27]


[13] A. Bianciotto, V. F. Carena, R. Gaudino, "EDFA gain transients: experimental demonstration of a low cost electronic control", IEEE Photon Technol Lett., vol. 15, pp. 1351-1353, 2003.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 26]


[14] T. Kinoshita, S. Kinoshita, "Analysis and control of transient dynamics of EDFA pumped by 1480 and 980 nm lasers", J Lightwave Technol., vol. 21, pp. 1728-1734, 2003.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 75]


[15] G. Luo, J. L. Zyskind, J. A. Nagel, M. Ali, "Experimental and theoretical analysis of relaxation-oscillations and spectral hole burning effects in all-optical gain clamped EDFA’s WDM networks", J Lightwave Technol., vol. 16, pp. 527-533, 1998.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 85]


[16] J. L. Shen, S. K. Wei, C. Y. Lin, S. I. Li, C. C. Huang, "High efficiency automatic-power-controlled and gain-clamped EDFA for broadband passive optical networking systems", J Infrared Milli Terahz Waves, vol. 31, pp. 490-499, 2010.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]


[17] K. Motoshima, N. Suzuki, K. Shimizu, K. Kasahara, T. Kitayama, T. Yasui, "A channel-number insensitive Erbium-doped fiber amplifier with automatic gain and power regulation function", J. Lightw. Technol., vol. 19, pp. 1759-1766, 2001.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 26]


[18] M. Fukutoku, M. Jinno, "Pump power reduction of optical feedback controlled EDFA using electrical forward control", in Proc. Opt. Amplifiers Their Appl. Tech. Dig., pp. 32-34, Vail, CO, 1998.

[19] S. Sergeyev, E. Vanin, G. Jacobsen, "Gain clamped dynamics in EDFA with combined electronic feed-forward-optical feedback control", in Proc. OFC Tech. Dig., pp. 518-519, Anaheim, CA, 2002.
[CrossRef]


[20] M. Yucel, H. H. Goktas, F. V. Celebi, "Design and implementation of fuzzy logic based automatic gain controller for EDFAs", Optik, vol. 125, pp. 5450-5453, 2014.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]


[21] V. Cherkassky, "Fuzzy Inference Systems: A Critical Review, Computational Intelligence: Soft Computing and Fuzzy-NeuroIntegration with Applications", Springer, Berlin, 1998.
[CrossRef]


[22] M. Buragohain, M. Chitralekha, "A novel approach for ANFIS modelling based on full factorial design", Applied Soft Computing, vol. 8, pp. 609-625, 2008.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 107]


[23] J. S. R. Jang, C. T. Sun, "Neuro-fuzzy modeling and control", Proceedings of the IEEE, vol. 83, pp. 378-406, 1995.
[CrossRef] [Web of Science Times Cited 945] [SCOPUS Times Cited 1326]


[24] H. Zheng, B. Jiang, L. Hongfei, "An adaptive neural-fuzzy inference system (ANFIS) for detection of bruises on Chinese bayberry (Myrica rubra) based on fractal dimension and RGB intensity color", Journal of Food Engineering, vol. 104, pp. 663-667, 2011.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 21]


[25] S. Altug, M.Y. Chow, H.J. Trussell, "Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis", IEEE Trans. Industrial Electronics, vol. 46, pp. 1069-1079, 1999.
[CrossRef] [Web of Science Times Cited 90] [SCOPUS Times Cited 123]


[26] H. Q. Zang, L. Qian, "The Automatic Temperature System With Fuzzy Self-adaptive PID control In Semiconductor Laser", in Proc., IEEE Int. Conf. on Automation and Logistics, pp. 1691-1694, Shenyang, China, 2009.
[CrossRef] [SCOPUS Times Cited 8]


[27] P. H. Tang, R. Song, G. Z. Chai, C. Y. Feng, L. P. Li, "Optimization of laser ablation technology for PDPhSM matrix nanocomposite thin film by artificial neural networks-particle swarm algorithm", J. Wuhan Unv. Tech.-Materials Sci. Edition, vol. 25, pp. 188-193, 2010.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[28] M. Yucel, H. H. Goktas, F. V. Celebi, "Temperature independent length optimization of L-band EDFAs providing flat gain", Optik, vol. 122, pp. 872-876, 2011.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 20]


[29] N. Celebi, "An accurate single CAD model based on radial basis function network", Optoelectron. Adv. Mater. - Rapid Commun., vol. 4, pp. 498-501, 2010.

[30] N. Celebi, "A complete type II quantum cascade laser model with the use of RBFN", Optoelectron. Adv. Mater. - Rapid Commun., vol. 7, pp. 188-190, 2013.

[31] S. Yigit, R. Eryigit, F. V. Celebi, "Optical gain model proposed with the use of artificial neural networks optimised by artificial bee colony algorithm", Optoelectron. Adv. Mater. - Rapid Commun., vol. 5, pp. 1026-1029, 2011.

[32] S. Tankiz, F.V. Celebi, R. Yildirim, "Computer aided design model for a quantum- cascade laser", IET Circuits, Devices and Systems, vol. 5, pp. 143-147, 2011.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 13]


[33] E. Akbari, Z. Buntat, E. Shahraki, A. Zeinalinezhad, M. Nilashi, "ANFIS modeling for bacteria detection based on GNR biosensor", Journal Of Chemical Technology and Biotechnology, vol. 91, pp. 1728-1736, 2016.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[34] F. V. Celebi, M. Yucel, H. H. Goktas. "Fuzzy logic based device to implement a single CAD model for a laser diode based on characteristic quantities", Optik, vol. 123, pp. 471-474, 2012.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]


[35] I. H. Choi, J. M. Pak, C. K. Ahn, S. H. Lee, M. T. Lim, M. K. Song, "Arbitration algorithm of FIR filter and optical flow based on ANFIS for visual object tracking", Measurement, vol. 75, pp. 338-353, 2015.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]


[36] S. S. Chong, A. R. A. Aziz, S. W. Harun, H. Arof , S. Shamshirband, "Application of multiple linear regression, central composite design, and ANFIS models in dye concentration measurement and prediction using plastic optical fiber sensor", Measurement, vol. 74, pp. 78-86, 2015.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 8]


[37] R. Zakaria, O. Y. Sheng, K. Wern, S. Shamshirband, D. Petkovic, N. T. Pavlovic, "Adaptive Neuro-Fuzzy Evaluation of the Tapered Plastic Multimode Fiber-Based Sensor Performance With and Without Silver Thin Film for Different Concentrations of Calcium Hypochlorite", IEEE Sensors Journal, vol. 14, pp. 3579-3584, 2014,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 4]


[38] S. Yigit, B. Tugrul, F. V. Celebi, "A complete CAD model for type-I quantum cascade lasers with the use of artificial bee colony algorithm", Journal of Artificial Intelligence, vol. 5, pp. 76-84, 2012.
[CrossRef] [SCOPUS Times Cited 12]


[39] D. Petkovic, S. Shamshirband, N. B. Anuar, M. H. N. M. Nasir, N. T. Pavlovic, S. Akib, "Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system", Infrared Physics & Technology, vol. 65, pp. 54-60, 2014.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[40] R. Zakaria, O. Y. Sheng, K. Wern, S. Shamshirband, A. W. A. Wahab, D. Petkovic, H. Saboohi, "Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies-a comparative study", Journal of the Optical Society of America A-Optics Image Science and Vision, vol. 31, pp. 1023-1030, 2014.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[41] S. Palani, U. Natarajan, M. Chellamalai, "On-line prediction of micro-turning multi-response variables by machine vision system using adaptive neuro-fuzzy inference system (ANFIS)", Machine Vision and Applications, vol. 24, pp. 19-32, 2013.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 10]


[42] H. Zhao, S. Fang, B. Shang, "Adaptive neuro-fuzzy inference system for generation of diffuser dot patterns in light guides", Applied Optics, vol. 49, pp. 2694-2702, 2010.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 3]


[43] F. V. Celebi, M. Yucel, H. H. Goktas, K. Danisman, "Intelligent modelling of alpha (a) parameter, comparison of ANN and ANFIS cases", Optoelectronics And Advanced Materials - Rapid Communications, vol. 7, pp. 470 - 474, 2013.

[44] F. V. Celebi, T. Altindag, R. Yildirim, L. Gokrem, "Semiconductor Laser Modeling with ANFIS", in Proc. 3rd International Conference on Application of Information and Communication Technologies, Baku, Azerbaijan, 2009.
[CrossRef] [SCOPUS Times Cited 4]


[45] S. Guillauma, "Designing fuzzy inference systems from data: an interpretability-oriented review", IEEE Transactions Fuzzy Syst., vol. 9, pp. 426-443, 2001.
[CrossRef] [Web of Science Times Cited 309] [SCOPUS Times Cited 417]


[46] J. S. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system", IEEE Transactions Syst. Man. and Cybernetics, vol. 23, pp. 665-685, 1993.
[CrossRef] [Web of Science Times Cited 5237] [SCOPUS Times Cited 7705]


[47] Z. Soozanchi-K, M. Yaghobi, M. R. Akbarzadeh-T, M. Habibipour, "Modeling and forecasting short-term electricity load based on multi adaptive neural-fuzzy inference system by using temperature", 2nd International Conf. on Signal Process. Syst., 2010.
[CrossRef] [SCOPUS Times Cited 2]


[48] J. S. R. Jang, "Self-learning fuzzy controllers based on temporal backpropagation", IEEE Transactions on Neural Networks, vol. 3, pp. 714-723, 1992.
[CrossRef] [Web of Science Times Cited 447] [SCOPUS Times Cited 580]


[49] F. M. Ham, I. Kostanic, Principles of Neurocomputing for Science and Engineering, Mc-Graw Hill, pp. 106-120, 2001.

[50] M. Sugeno, G. T. Kang, "Structure identification of fuzzy model", Fuzzy Sets and Systems, vol. 28, pp. 15-33, 1988.
[CrossRef] [Web of Science Times Cited 1276] [SCOPUS Times Cited 1532]


[51] M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control", IEEE Transactions on Systems, Man, and Cybernetics vol.15, issue 1, pp. 116-132 1985.
[CrossRef] [SCOPUS Times Cited 11437]


[52] K. Kaveh., M. D. Bui, P. Rutschmann, "New hybrid learning algorithms in adaptive neuro fuzzy inference systems for contraction scour modeling", in Proc. of the 14th International Conference on Environmental Science and Technology Rhodes, Greece, 2015.



References Weight

Web of Science® Citations for all references: 8,758 TCR
SCOPUS® Citations for all references: 23,724 TCR

Web of Science® Average Citations per reference: 165 ACR
SCOPUS® Average Citations per reference: 448 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

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