首页|Findings on Support Vector Machines Detailed by Investigators at HeNan Polytechn ic University (Fault Detection Method for Flexible Dc Grid Based On Ceemdan Mult iscale Entropy and Ga-svm)
Findings on Support Vector Machines Detailed by Investigators at HeNan Polytechn ic University (Fault Detection Method for Flexible Dc Grid Based On Ceemdan Mult iscale Entropy and Ga-svm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Fresh data on Machine Learning - Suppo rt Vector Machines are presented in a newreport. According to news originating from Jiaozuo, People’s Republic of China, by NewsRx correspondents,research sta ted, “Compared with the traditional AC grid, the flexible DC grid has the advant ages of lowwire loss and large transmission capacity, but it is difficult to ex tract fault signals and diagnose variousfaults. Therefore, a fault detection me thod based on complete ensemble empirical mode decompositionwith adaptive noise analysis (CEEMDAN) multiscale entropy (MSE) and genetic algorithm optimizationsupport vector machine (GA-SVM) is proposed.”
JiaozuoPeople’s Republic of ChinaAsi aMachine LearningSupport Vector MachinesHeNan Polytechnic University
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