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

Yücel, Murat; Çelebi, Fatih V.; Torun, Murat; Göktaş, H. Haldun

doi:10.4316/aece.2017.01003

Cited by 6 publications

(10 citation statements)

References 35 publications

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“…The biggest challenge in FIS is to define a rule base [35][36][37]. Jyh-Shing and Jang suggested an optimization of the FIS parameters through the use of ANN as a solution [38]. In this method, ANN promotes decision-making with a Takagi-Sugeno type "if, then" rule table . The linguistic expressions of a Sugeno type fuzzy model with two inputs as x and y are as follows [35][36][37][38]:…”

Section: Lumen Maintenance Degradation (L 70mentioning

confidence: 99%

“…It determines the membership value of each input. The nodes in the input layer are expressed as follows: The second layer provides the activation of the fuzzy rules, and it is where the data from the first layer is multiplied [35][36][37][38]:…”

Section: Adaptivementioning

confidence: 99%

“…The third one is the normalization layer, and it provides the normalization of the firing strength of the fuzzy rules [35][36][37][38]:…”

Section: Adaptivementioning

confidence: 99%

“…The fourth layer is the defuzzification layer. It is obtained by multiplying the linear function or the constant determined for each rule through the normalized firing strength [35][36][37][38]:…”

Section: Adaptivementioning

confidence: 99%

See 3 more Smart Citations

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Kıyak

Gökmen

Koçyiğit

2021

Journal of Nanomaterials

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Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

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Section: Lumen Maintenance Degradation (L 70mentioning

confidence: 99%

Section: Adaptivementioning

confidence: 99%

“…The third one is the normalization layer, and it provides the normalization of the firing strength of the fuzzy rules [35][36][37][38]:…”

Section: Adaptivementioning

confidence: 99%

Section: Adaptivementioning

confidence: 99%

See 2 more Smart Citations

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Kıyak

Gökmen

Koçyiğit

2021

Journal of Nanomaterials

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“…Therefore, the random nature is associated with the fuzzy controller and may not result in optimum results. But, in ANFIS controller, proper rule base can be selected because it utilizes both the fuzzy logic principles and neural networks principles [21][22] and gives excellent results. The fuzzy logic is 89% generally in agreement with results given by experts.…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of a Single-Stage CUK Based Three Phase Photovoltaic Inverter using Anfis Controller

Annapoorani¹,

Jayaparvathy²,

Priyanka³

2019

IJITEE

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A modular structured and high efficient photovoltaic (PV) system is essential in today’s scenario. The single stage Cuk based inverter has continuous input and output current, and hence, makes it suitable for applying MPPT techniques when used for PV applications. The PI, PID, and fuzzy controllers could be applied for PV inverter. The PI controller decreases the error in steady state, and at the same time, it also decreases the stability of the system. The PID controller involves large time delay process. The random nature in fuzzy controller may not lead to optimum results. Hence, this paper proposes a controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for a three phase PV inverter based on Cuk converter. The effectiveness of proposed system is verified using MATLAB/SIMULINK, and the results are presented. The performance of proposed ANFIS controller for Cuk based three phase inverter is compared with conventional PI controller. The proposed system has several merits like increased performance, accuracy, and efficiency.

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The design and implementation of a software based gain control for EDFAs used in long-haul optical networks

Akcesme

Yücel

Burunkaya

2021

Optik

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

Cited by 6 publications

References 35 publications

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Performance Enhancement of a Single-Stage CUK Based Three Phase Photovoltaic Inverter using Anfis Controller

The design and implementation of a software based gain control for EDFAs used in long-haul optical networks

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