Optimization of digital holographic setup by a fuzzy logic prediction system

Kaya, Gulhan Ustabas; Erkaymaz, Okan; Saraç, Zehra

doi:10.1016/j.eswa.2016.03.019

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Penelitian terdahulu dengan melakukan desain smart meter untuk memantau dan mengidentifikasikan pemakaian energi listrik pada sektor rumah tangga menggunakan backpropagation neural network [11] yang diterapkan pada penggunaan peralatan elektronik secara real time. Sistem monitoring tersebut di implementasikan dengan cara scanning subnetmask IP (Internet Protocol) perangkat node yang telah terhubung dari alamat IP ke-1 hingga 255.…”

Section: Pendahuluanunclassified

“…Contohnya Xc1 titik terpilih sebagai pusat cluster, Dc1 menjadi ukuran densitas. Kemudian densitas titik disekitarnya dikurangi menjadi sebagai berikut [11]:…”

Section: Gambar 1 Diagram Alur Perhitungan Metodeunclassified

“…Sedangkan reject ratio yaitu batas atas, titik data calon pusat cluster tidak dapat dijadikan pusat cluster. Terdapat tiga kondisi pada suatu iterasi [11]:…”

Section: Gambar 1 Diagram Alur Perhitungan Metodeunclassified

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Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

Hikmiyah¹,

Siregar²,

Prayitno³

et al. 2021

petir

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The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA

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Section: Pendahuluanunclassified

“…Contohnya Xc1 titik terpilih sebagai pusat cluster, Dc1 menjadi ukuran densitas. Kemudian densitas titik disekitarnya dikurangi menjadi sebagai berikut [11]:…”

Section: Gambar 1 Diagram Alur Perhitungan Metodeunclassified

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Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

Hikmiyah¹,

Siregar²,

Prayitno³

et al. 2021

petir

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“…Kaya et al presented a study for optimization of a digital holographic setup by a fuzzy logic prediction system. During the optimization process, the authors also tried to decrease the required time of the optimization process [26].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Whale Optimization Algorithm for Numeric Optimization

Kaya

Kilic

BabaoÄŸlu

et al. 2021

MJCS

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Meta-heuristic approaches are used as a powerful tool for solving numeric optimization problems. Since these problems are deeply concerned with their diversified characteristics, investigation of the utilization of algorithms is significant for the researchers. Whale optimization algorithm (WOA) is one of the novel meta-heuristic algorithms employed for solving numeric optimization problems. WOA deals with exploitation and exploration of the search space in three stages, and in every stage, all dimensions of the candidate solutions are updated. The drawback of this update scheme is to lead the convergence of the algorithm to stack. Some known meta-heuristic approaches treat this issue by updating one or a predetermined number of dimensions in their update scheme. To improve the exploitation behavior of WOA, a fuzzy logic controller (FLC) based adaptive WOA (FAWOA) is suggested in this study. An FLC realizes the update scheme of WOA, and the proposed FLC determines the rate of the change in terms of dimension. The suggested FAWOA is evaluated using 23 well-known benchmark problems and compared with some other meta-heuristic approaches. Considering the benchmark problems, FAWOA achieves best results on 11 problem and only differential evaluation algorithm achieve best results on 10 problems. The rest of the algorithms couldn’t achieve the best results on not more than 5 problems. Besides, according to the Friedman and average ranking tests, FAWOA is the first ranked algorithm for solving the benchmark problems. Evaluation results show that the suggested FAWOA approach outperforms the other algorithms as well as the WOA in most of the benchmark problems.

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“…This law was designed to control road tunnel ventilation. Kaya et al (2016) presented a fuzzy logic based prediction system for the optimization of digital holographic setup and compared the optimization results of experimental system with those of fuzzy logic prediction system. Vafaie et al (2014) presented a genetic-fuzzy system for prediction of heart diseases.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based Time Series Prediction Algorithm Using Nearest Neighborhood Clustering

Irum

Muhammad²

2020

JER is an international, peer-reviewed journal that publishes f

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This paper presents a fuzzy logic based prediction algorithm using nearest neighborhood clustering for the prediction of time series. The proposed algorithm has been developed by using nearest neighborhood clustering scheme and optimal fuzzy system. The presented algorithm is tested by the prediction of Mackey-Glass time series, time series obtained from Logistic Map equation, and an oil refinery data series. To the best of our knowledge, very few studies have attempted to predict Logistic Map. The simulation results show that the proposed algorithm is able to predict linear, nonlinear, and even chaotic time series to a reasonable accuracy. It performs well, even when the mathematical model of time series does not exist.

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Optimization of digital holographic setup by a fuzzy logic prediction system

Cited by 7 publications

References 9 publications

Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

Fuzzy Adaptive Whale Optimization Algorithm for Numeric Optimization

Fuzzy Logic Based Time Series Prediction Algorithm Using Nearest Neighborhood Clustering

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