Electrical Engineering Using Ls Svm by Abduesslam Abedou (9 results)
Electrical Engineering (Voltage Problem) Using LS-SVM and LVQ
Abduesslam. M. Abedou, Khaled; Nizam. M. T, Muhammad; Inayati, .
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Paperback. Condition: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock.
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Taschenbuch. Condition: Neu. Electrical Engineering (Voltage Problem) Using LS-SVM and LVQ | Khaled Abduesslam. M. Abedou (u. a.) | Taschenbuch | 56 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330083127 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[…at]preigu[dot]de | Anbieter: preigu.
Language: English
Published by LAP LAMBERT Academic Publishing Mai 2017, 2017
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Classification of Voltage Problem Using Support Vector Machine (SVM) of electrical power system using Least Squares Support Vector Machine (LS-SVM) algorithm and implemented on IEEE-39 bus New-England system. The data was collec…ted from the time domain simulation by using input to the LS-SVM classification, and LS-SVM PTSI estimation on Least Squares Support Vector Machine, which is used as a predictor to determine the dynamic voltage collapse indices by increasing of the power in load buses. The Kernel function type and Kernel parameter are considered. In order to verify the effectiveness of the proposed LS-SVM classification and estimation method, its performance is compared with the Learning Vector Quantization (LVQ). 56 pp. Englisch.
Electrical Engineering (Voltage Problem) Using LS-SVM and LVQ
Abduesslam. M. Abedou, Khaled; Nizam. M. T, Muhammad; Inayati, .
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Electrical Engineering (Voltage Problem) Using LS-SVM and LVQ
Abduesslam. M. Abedou, Khaled; Nizam. M. T, Muhammad; Inayati, .
- Softcover
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Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Abduesslam. M. Abedou Khaled- Khaled Abduesslam M. Graduated master degree from Sebelas Maret University (UNS), Central Java, Indonesia. (2014).- Prof. Muhammad Nizam. M. T. PhD, Head of control unit UN…S Ex.- Inayati, ST., MT., PhD. .
Language: English
Published by LAP LAMBERT Academic Publishing Mai 2017, 2017
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Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Classification of Voltage Problem Using Support Vector Machine (SVM) of electrical power system using Least Squares Support Vector Machine (LS-SVM) algorithm and implemented on IEEE-39 bus New-England system. The data was collected…from the time domain simulation by using input to the LS-SVM classification, and LS-SVM PTSI estimation on Least Squares Support Vector Machine, which is used as a predictor to determine the dynamic voltage collapse indices by increasing of the power in load buses. The Kernel function type and Kernel parameter are considered. In order to verify the effectiveness of the proposed LS-SVM classification and estimation method, its performance is compared with the Learning Vector Quantization (LVQ).VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Classification of Voltage Problem Using Support Vector Machine (SVM) of electrical power system using Least Squares Support Vector Machine (LS-SVM) algorithm and implemented on IEEE-39 bus New-England system. The data was collected f…rom the time domain simulation by using input to the LS-SVM classification, and LS-SVM PTSI estimation on Least Squares Support Vector Machine, which is used as a predictor to determine the dynamic voltage collapse indices by increasing of the power in load buses. The Kernel function type and Kernel parameter are considered. In order to verify the effectiveness of the proposed LS-SVM classification and estimation method, its performance is compared with the Learning Vector Quantization (LVQ).
